DRAFT FINAL
&


Failure Rate Analyses and
Development of Fast-Pass,
Retest, and CPP Algorithms for
IM147 Max CO Cutpoints
prepared for:
U.S. Environmental Protection Agency
December 14, 1999
prepared by:
Sierra Research, Inc.
1801 J Street
Sacramento, California 95814
(916) 444-6666

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Failure Rate Analyses and Development of
Fast-Pass, Retest, and CPP Algorithms
for IM147 Max CO Cutpoints
prepared for:
U.S. Environmental Protection Agency
Regional and State Programs Division
Office of Mobile Sources
Under Contract No. 68-C7-0051
Work Assignment No. 1-08
December 14, 1999
prepared by:
Richard W. Joy
Garrett D. Torgerson
Thomas R. Carlson
Sierra Research, Inc.
1801 J Street
Sacramento, CA 95814
(916) 444-6666

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DISCLAIMER
Although the information described in this report has been funded wholly or in part
by the United States Environmental Protection Agency under Contract No. 68-C7-0051,
it has not been subjected to the Agency's peer and administrative review and is being
released for information purposes only. It therefore may not necessarily reflect the
views of the Agency and no official endorsement should be inferred.

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Failure Rate Analyses and Development of
Fast-Pass, Retest, and CPP Algorithms
for IM147 Max CO Cutpoints
Table of Contents
page
1.	Summary 	1
Scope of Work	2
Projected IM147 Failure Rates 	>	3
Modal IM147 Fast-Pass Standards 	4
Predictive Retest Algorithms	5
Modal Fast-Fail Criteria	6
Integrated Fast-Pass, Retest, and Fast-Fail Algorithm Results 	7
Segment 2 Revised Integrated Algorithms Results /		10
Development of IM147 Driver Variation Standards	10
Integration of CPP Variation Limits 	10
SIP Credit Analysis 	11
Need for Follow-Up Analysis 	12
2.	Introduction 	i3
Scope	16
Organization of the Report		18
3.	Test Data 		19
4.	IM147 Cutpoint Analysis			24
IM147 Test Length 			 . 24
Cutpoint Analysis 	25
Comparison of Failure Rates	28
5.	Optimized EM147 Test Criteria	34
Modal Fast-Pass Standards 			34
Predictive Retest Algorithms	39
Modal Fast-Fail Criteria	46
Integration of Fast-Pass, Retest, and Fast-Fail Algorithms	48
Segment 2 Revised Integrated Algorithms Results	51
SIP Credit Analysis 			51
Need for Follow-Up Analysis 		52

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Table of Contents (continued)
page
6.	Development of IM147 Variation Limits 	53
Existing Tolerance Limits 	53
Previous Analysis of Speed Variation Limits 	55
Development of Positive Power-Based Variation Limits 	56
Evaluation of IM147 Variation Limits on Test Time 	64
7.	Integration of CPP Variation Limits 	66
8.	References 	69
Appendix A - Startup, Intermediate and Final IM240 and IM147 Cutpoints
Appendix B - Max CO, Startup, Intermediate and Final IM147 Failure Rates
Appendix C - IM147 Regression Coefficients
Appendix D - Excess Emissions Identification for Max CO Cutpoints
Appendix E - Second-by-Second CPP Variation Limits
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List of Tables
page
1-1 Failure Rates, Third IM147	3
1-2 Modeled Fast-Pass Results Excess Emissions vs. Average Test Time 	5
1-3 Retest Algorithm Results 		6
1-4 Fast-Fail Algorithm	 	7
1-5 Comparison of Integrated Algorithms vs. Standard IM147 Impact on
Test Time and Excess Emissions Lost 	9
1-6 Average Test Time by Test Time Reduction Methodology and CPP 	11
1-7 Comparison of IM147 Max CO Cutpoints to IM240 Final Standards
Impact on Excess Emissions Lost	12
4-1	IM240 to IM147 Composite Regression Equation Coefficients	25
4-2	IM147 Composite to Phase 2 Regression Equation Coefficients	27
4-3	Revised IM147 Max CO Cutpoints (Composite/Phase 2) 	28
4-4	Failure Rates, Third IM147	29
5-1	Revised IM147 Segments	36
5-2	Modeled Fast-Pass Results Excess Emissions vs. Average Test Time 	38
5-3	Excess Emissions Indentified (With and Without Fast-Pass) 		39
5-4	Retest Algorithm Results 		46
5-5	Fast-Fail Algorithm	48
5-6	Comparison of Integrated Algorithms (No CPP) vs. Standard IM147	49
5-7	Comparison of IM147 Max CO Cutpoints to IM240 Final Standards	52
6-1	Preliminary CPP Cutpoints Based on Top-50% Drivers 	61
6-2	Centered CPP Cutpoints and Resulting Test Abort Rates 		62
7-1	End Test Decisions Affected by CPP Errors 				66
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List of Figures
page
1-1 Modal IM147 Fast-Pass Standards	4
1-2 Integrated Test Results Maximum CO Cutpoints Without CPP Limits	8
3-1 Test Sequence Used to Investigate Triplicate
IM147 Tests in Arizona Test Lanes 	19
3-2	Model Year Distribution of Sample Fleet and AZ Overall Fleet	22
4-1	IM147 Trace Phase 1 and Phase 2 	25
4-2	Failure Rate by Consecutive IM 147 Max CO Cutpoints	30
4-3	Failure Rate by Consecutive IM147 Startup Cutpoints 	31
4-4	Failure Rate by Consecutive EM 147 Intermediate Cutpoints	32
4-5	Failure Rate by Consecutive IM147 Final Cutpoints	33
5-1	IM147 Test Segments Used for Fast-Pass Cutpoint Development	37
5-2	Retest Algorithm During First IM147-LDGV 		42
5-3	Retest Algorithm During Second IM147-LDGV	43
5-4 Retest Algorithm During First IM147-LDGT1, LDGT2 	44
5-5	Retest Algorithm During Second IM147-LDGT1, LDGT2	45
5-6	Integrated Test Results Max CO Cutpoints Without CPP Limits 	50
6-1	Comparison of IM 147 Variation Limit Metrics	58
6-2 Distribution of Arizona IM147 Positive Power Differences 				60
6-3 Illustration of Second-by-Second IM147 Cumulative Positive
Power Variation Limits		63
6-4	Effect of Initial CPP Limits Multiplier on Effective IM147 Abort Rate 	64
7-1	No Title			68
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1. SUMMARY
Under the Clean Air Act Amendments of 1990, metropolitan areas with the most serious
air quality problems are required to implement so-called "enhanced" I/M programs. Two
different test procedures for exhaust emissions testing in enhanced programs have been
approved by EPA: the "IM240" test, and the "Acceleration Simulation Mode" (ASM)
test. With either procedure, the efficiency of the testing process depends on how quickly
accurate decisions can be made as to whether a vehicle should pass or fail.
Inadequate vehicle preconditioning has previously been identified as a cause of false
failures in I/M programs. In fact, previous EPA and DEQ analyses estimate that 25% of
the vehicles failing the final IM240 standards would pass with further preconditioning,
and that these vehicles can be identified through modal analysis. To address this
problem, Sierra suggested, in another study, that Phase 1 of the IM240 test be eliminated,
instead using only the second hill of the IM240 (i.e., the "IM147") up to three times in
succession to ensure adequate preconditioning. Based on this recommendation, and to
address the issue of inadequate preconditioning without compromising test throughput
excessively, Arizona therefore decided to change its test procedure to the IM147
beginning January 1, 2000. This program upgrade will also include implementation of
"Max CO" cutpoints previously developed by Sierra, which are designed to maximize the
CO benefits of the program.
Regarding this proposed alternative to the existing IM240 test, several concerns needed to
be addressed prior to implementation in Arizona. First of all, how would emissions
identification and credit change with the new procedure. Secondly, because the length of
the proposed test may be equivalent to that of three IM147 tests, it can use considerably
more dynamometer time than the other aforementioned tests, thus increasing the cost of
the program. Prior to this study, two other studies have already been conducted to start
addressing these issues.
The first study addressed, among other things, reducing test time and projected emission
credit levels for the IM147 test. Part of the data analyzed in this study consisted of 101
tests where vehicles were given three back-to-back IM147 tests. These data were used to
create "Phase 2b" cutpoints, which are analogous to phase 2 cutpoints for the IM240 test,
thus giving vehicles two ways to pass. In addition, fast-pass cutpoints for both the entire
IM147 as well as Phase 2b were also developed. The other data used in this study
consisted of 2% random sample IM240 test data collected in the Arizona IM240 program.
This, in combination with the 101 vehicle data, was used to project emission credits.
Unfortunately, since this study did not include back-to-back IM147 to IM240 testing,
excess emissions identification rates and SIP credit could not be conclusively established.
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In a follow-up work assignment in 1998,'* EPA asked Sierra to evaluate 304 triplicate
IM147 tests followed by an IM240 test. These data were analyzed to verify the
preliminary excess emission identification rates and average test time.estimates projected
for the Arizona IM program from the Phase 2 and IM240 data sets collected in previous
studies. In addition, improved fast-pass and retest algorithms were developed for the
IM147 test using the same approach used previously in developing similar IM240
algorithms; however, the cutpoints scenarios evaluated in the study did not include the
Max CO cutpoints previously developed for DEQ.
PKE Speed Variation Criteria - In the 1997 IM240-related evaluation for EPA (SR98-02-
01),2 Sierra developed improved speed variation criteria based on the total Positive
Kinetic Energy (PKE) change per mile traveled during the IM240 cycle. These criteria
were designed to minimize the variation in emissions while still being feasible for use by
minimally trained drivers with a reasonable aptitude for dynamometer driving. However,
only IM240 drive cycle criteria were developed in the 1997 study. Therefore, further
analysis was needed to develop similar speed variation criteria for the IM147.
Scope of Work
To aid in the Arizona IM147 implementation effort, EPA issued a work assignment
(#1-08) to Sierra to complete the following tasks:
1.	Develop projected IM147 failure rates for the Arizona I/M program;
2.	Develop modal IM147 fast-pass standards for the Max CO cutpoints;
3.	Develop modal predictive IM147 retest algorithms for the Max CO cutpoints;
4.	Develop modal IM147 fast-fail criteria; and
5.	Develop fast-pass and full-duration PKE criteria for the EM 147 test.
Three distinct data sets were used in this study. The first two sets were the 304 vehicle
study, collected for SR99-10-02, and a 543-vehicle sample collected for this study. For
both of these sets, randomly selected vehicles were given triplicate IM147 tests followed
by an IM240 test. The third data set comprised 2518 vehicles given triplicate IM147 tests
and, if they failed the third test, an IM240 test. After removing invalid tests, the test data
sets used for this study consisted of 300, 535, and 2512 vehicles respectively (i.e., 3347
vehicles total). All of the data were collected by Gordon-Darby I/M lanes in Phoenix,
Arizona.
' Superscripts denote references listed in Section 8.
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Projected IM147 Failure Rates
Prior to projecting failure rates for the IM147 test using each of the four set of emissions
standards (Startup, Intermediate, Final, Max CO), Max CO outpoints developed as part of
SR99-10-02 were revised using the combined 300- and 535-vehicle data sets. To this
end, IM147 scores were regressed against IM240 scores. The resulting regression
equations were then used to derive IM147 cutpoints from the IM240 cutpoints. IM147
phase 2 cutpoints were developed similarly by regressing IM147 composite scores
against IM147 phase 2 scores. A scaling factor of 0.9 was multiplied against the
predicted phase 2 cutpoint to make the phase 2 cutpoint slightly more stringent than the
composite cutpoint, as it is with the IM240 test. Table 4-4 in Section 4 shows the revised
Max CO cutpoints.
After revising the Max CO cutpoints, failure rates for the IM147 test using the Startup,
Intermediate, final, and Max CO cutpoints were determined using the 3347-vehicle data
set. These failure rates, which are shown in Table 1-1, were based upon the results of the
third IM147 test.
Table 1-1
Failure Rates, Third IM147

HC
CO
NOx
OVERALL
Vehicle
Type
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass .
% Fail
Max CO Cutpoints
LDGV
114
1666
6.4%
250
1530
14.0%
155
1625
8.7%
390
1390
21.9%
LDGT1
49
915
5.1%
156
808
16.2%
70
894
7.3%
219
745
22.7%
LDGT2
25
578
4.1%
63
540
10.4%
30
573
5.0%
93
510
15.4%
All
188
3159
5.6%
469
2878
14.0%
255
3092
7.6%
702
2645
21.0%
Startup Cutpoints
LDGV
106
1674
6.0%
130
1650
7.3%
141
1639
7.9%
281
1499
15.8%
LDGT1
52
912
5.4%
33
931
3.4%
48
916
5.0%
108
856
11.2%
LDGT2
33
570
5.5%
30
573
5.0%
28
575
4.6%
67
536
11.1%
All
191
3156
5.7%
193
3154
5.8%
217
3130
6.5%
456
2891
13.6%
Intermediate Cutpoints
LDGV
157
1623
8.8%
161
1619
9.0%
196
1584
11.0%
367
1413
20.6%
LDGT1
80
884
8.3%
53
911
5.5%
73
891
7.6%
165
799
17.1%
LDGT2
40
563
6.6%
42
561
7.0%
42
561
7.0%
93
510
15.4%
All
277
3070
8.3%
256
3091
7.6%
311
3036
9.3%
625
2722
18.7%
Final Cutpoints
LDGV
280
1500
15.7%
240
1540
13.5%
277
1503
15.6%
507
1273
28.5%
LDGT1
120
844
12.4%
85
879
8.8%
127
837
13.2%
239
725
24.8%
LDGT2
73
530
12.1%
56
547
9.3%
82
521
13.6%
149
454
24.7%
All
473
2874
14.1%
381
2966
11.4%
486
2861
14.5%
895
2452
26.7%
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This table helps clarify the relationship between the four sets of outpoints. The Startup,
Intermediate, and Final outpoints result in nearly equal HC, CO, and NOx failure rates, by
vehicle category, which increase as the stringency of the cutpoints increase. The Max CO
standards, when compared to the final standards, result in lower HC and NOx failure
rates, and higher CO failure rates, especially for light-duty trucks.
Figure 1-1 shows the failure rates for each of the three IM147s, based on the Max CO
standards. As the figure shows, most of the decrease in failure rates due to the use of
multiple test cycles (i.e., to address the lack of adequate preconditioning) occurs between
the first and second IM147s. As a result, it is reasonable to expect that algorithms
designed to shorten the test would often end the test prior to the third IM147. This is, in
fact, the case, as is shown later in this report.
Figure 1-1
Overall Failure Rate by Vehicle Type
Max CO Standards
ED 1st IM147
H2ndIM147
03rd IM147
PC
LDT1 LDT2
Vehicle Type
All
Modal IM147 Fast-Pass Standards
Using the same methodology employed in SR99-10-02, this study developed fast-pass
regression standards for the Max CO cutpoints. This methodology requires dividing the
IM147 test into a series of short segments over which emission mass is accumulated. By
performing multivariate linear regressions of these incremental segments, modal fast-pass
coefficients were developed to predict when vehicles would pass without having to
complete the entire test. Unlike the previous study, which divided the IM147 test into 14
segments, this study divided the test into 20 segments, thus increasing the frequency of
opportunities for fast-pass.
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While a fast-pass procedure of this nature has the ability to greatly reduce test time, this
reduction has to be balanced against false passes. False passes occur when vehicles that
would otherwise fail an inspection are fast-passed out because their emissions over the
drive cycle are not appropriately characterized by the regression. For this study, false
passes are quantified by measuring excess emissions, which are defined as emissions
collected during an IM240 test in excess of the applicable standard for a given vehicle.
The IM147 test receives credit for identifying excess emissions if it fails a vehicle that
had excess IM240 emissions when using the same emission outpoints (e.g. Max CO).
In this study, both the regression of the IM147 segments and the analysis of that
regression were performed using the combined data sets (3347 vehicles) and the Max CO
standards. Table 1-2 details the results of this analysis.
Table 1-2
Modeled Fast-Pass Results
Excess Emissions vs. Average Test Time

No Fast-Pass*
Fast-Pass Enabled
Excess HC Identified
94.3%
92.7%
Excess CO Identified
98.2%
94.3%
Excess NOx Identified
92.1%
91.2%
Average Test Time**
217 seconds
125 seconds
* While vehicles cannot terminate in the middle of an IM147 without the fast-pass algorithm, the test may
end prior to completing three IM147s if the emissions measured at the end of any one of the IM147s meet
the applicable standards.
**Test time refers to the time actually required to operate the vehicle on the dynamometer.
Predictive Retest Algorithms
The workplan for the study called for Sierra to refine algorithms originally developed for
SR99-10-02 and then to apply them to the total vehicle sample (3347 vehicles).to
determine their net effect on test time. In contrast to the fast-pass algorithm, where
misidentification results in false-passing vehicles and a loss in excess emissions
identification, the retest algorithm errors result in false failures, which can lead to
consumer complaints. Like the fast-pass algorithm, decreases in test time need to be
weighed against false failures to determine a reasonable compromise.
While the original retest procedure described in SR99-10-02 used a combination of mass
and concentration emissions measurements to anticipate whether a vehicle would benefit
from additional testing, concerns expressed by Gordon-Darby regarding the complexity
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of this algorithm led to a different approach for this analysis. Instead, a variation of the
fast-pass regression calculation was developed and used to predict emissions
improvement over an IM147 test. In short, the emissions result predicted after Segment 7
is compared to the emissions result predicted after Segment 19 to determine whether the
vehicle emissions are converging on the applicable standard.
Using this procedure, test time was reduced from 125 seconds (with fast-pass enabled) to
96 seconds. Table 1-3 details further results of the retest analysis.
Table 1-3
Retest Algorithm Results
LDGV LDGT1, LDGT2
Total Number of Complete
Tests
1567
1780
# of Failures Without
Retest Algorithm
327 (20.9% of 1567)
273 (15.3% of 1780)
# of Correctly Identified
Failures2
245 (74.9% of 327)b
180 (65.9% of 273)b
# Failing After 1 IM147
94 (38.4% of 245)c
97 (53.9% of 180)c
# Failing After 2 IM147s
151 (61.6% of 245)
83 (46.1% of 180)
# of Passing Vehicles
Falsely Failed by Retest
0(0% of 1567)
0(0% of 1780)
""Correctly identified failures" refers to those vehicles that were still failing at the end of the third
IM147.
bThe number shown in parentheses is the number of failures without the retest algorithm.
The number shown in parentheses is the total number of IM147 Cycle 2 and 3 failures.
Modal Fast-Fail Criteria
One of the requirements of this work assignment was to develop modal fast-fail criteria.
Unlike the retest procedure, which can terminate the test at the ends of the individual
IM147s, the fast-fail algorithm can terminate tests during an IM147 test.
Like the retest algorithm, errors committed by the fast-fail criteria result in false failures.
As a result, decreases in test time have to be weighed against false failures. With this in
mind, fast-failures cannot be made during the first of the three IM147s. Analysis showed
that the results of the first IM147 were too unpredictable relative to the final result to risk
false-failing vehicles. The final two IM147s, however, would serve reasonably well for
this purpose.
There are two different fast-fail algorithms, one for each of the final two IM147 tests.
The fast-fail algorithm for the second IM147 fails vehicles with excessively high
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predicted emissions after segment 7 of the second IM147. The fast-fail algorithm for the
third IM147 test uses a variation of the fast-pass algorithm to predict failing vehicles
throughout the test. Both of these algorithms are described more completely in the body
of this report.
Table 1-4 shows the results of the fast-fail algorithms when applied to the 3347 vehicle
sample. The fast-fail algorithm reduces average test time an additional 2 seconds, from
96 seconds (with fast-pass and retest enabled) to 94 (with fast-pass, retest, and fast-fail
enabled).

Table 1-4
Fast-Fail Algorithm

Vehicle
Class
Second IM147 Fast-
Failures
Third IM147 Fast-
Failures
False Failures
LDV
124
211
3
LDT
99
182
3
Total
223
393
6
Integrated Fast-Pass. Retest. and Fast-Fail Algorithm Results
This portion of the study combined all of the optimized algorithms to determine their net
effect on test time, false failures, and excess emissions while using the Max CO standards
with the 3347 vehicle sample. The flow chart contained in Figure 1-2 shows the point at
which vehicles concluded the test and the reason they passed or failed. It also indicates
the average dynamometer test time for each category of vehicles. Following the flow
chart, Table 1-5 shows the net effect of the procedures on test time and excess emissions.
The table shows that excess emission identification using the Max CO cutpoints with the
fast-pass, retest, or fail-fail algorithms enabled is 94.4%, 95.4%, and 95.7% for HC, CO,
and NOx, respectively. (For comparison, the respective identification rates are 94.3%,
98.2%, and 92.1% without the various test criteria enabled.) This is down from the
identification rates developed in SR99-10-02,1 which identified 99.6% of the HC, 98.2%
of the CO, and 99.9% of the NOx with the fast-pass and retest.algorithms enabled (fast-
fail was not considered). However, direct comparison of the two sets of results may not
be relevant for several reasons. First, the previous study measured excess emissions
captured against the Final Cutpoints rather than the Max CO cutpoints use for this study.
Second, because fast-fail was created for this study, it was not included in the previous
study results. Third, the retest algorithm has been modified as part of this study and will
therefore have a different effect on the results. Finally, the majority of the data used in
this study were collected with the newer model year exemptions in place. As a result, the
vehicle distribution was skewed toward older vehicles relative to that in the previous
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Figure 1-2
Integrated Test Results
Maximum CO Outpoints Without CPP Limits
TOTAL PASS = 2,802	TOTAL FAIL = 545
TOTAL FALSE FAILURES = 3
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Table 1-5
Comparison of Integrated Algorithms vs. Standard IM147
Impact on Test Time and Excess Emissions Lost*
Class
Model
Year
Group
Sample
Size"
Mean Test
Time
Standard"
Mean Test
Time w/
Algorithms"
% Excess Emissions
Identified"*
HC
CO
NOx
LDGV
1981-82
105
286:4
140.1
-
87.6%
100.0%
1983-85
228
311.2
169.4
100.0%
97.4%
100.0%
1986-89
425
248.4
112.0
99.3%
97.5%
100.0%
1990-95
952
184.8
78.3
96.9%
85.3%
91.4%
1996+
70
154.3
37.1
-
-
-
All
1780
221.0
100.0
98.6%
93.9%
97.6%
LDGT1
1981-85
260
306.6
158.7
75.7%
97.5%
98.4%
1986-89
222
230.8
101.6
92.4%
91.0%
99.6%
1990-95
450
173.3
59.2
0.0%
0.0%
100.0%
1996+
32
155.1
31.6
-
-
100.0%
All
964
221.9
94.9
79.3%
94.4%
98.7%
LDGT2
1981-85
94
307.5
158.8
100.0%
100.0%
100.0%
1986-87
64
253.2
101.4
100.0%
100.0%
70.8%
1988-95
427
166.5
54.2
100.0%
58.0%
39.7%
1996+
18
146.0
28.0
-
-
-
All
603
197.1
74.7
100.0%
98.5%
86.0%
Weighted Average
3347
217.0
94.0
94.4%
95.4%
95.7%
* Test time results do not include impact of driver variation limits.
** Mean test time standard refers to the average dynamometer test time without the algorithms enabled.
This was determined using the 3347-vehicle sample.
*** Percent of IM240 (Max CO) excess emissions identified with the integrated algorithms enabled. This
was determined using the 835-vehicle sample.
study. Less rigorous test criteria were also evaluated as part of this latest study.
However, their use yielded relatively little improvement in identification rate at the cost
of a large increase in test time. A decision was therefore made not to pursue this latter
option.
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Segment 2 Revised Integrated Algorithms Results
The original integrated algorithm results were initially determined assuming fast-pass and
fast-fail results could not be rendered prior to the fourth segment (i.e., no earlier than Test
Time = 28 seconds). This was consistent with the procedure established in SR98-02-01.
Gordon-Darby, wishing to further minimize test time, requested that Sierra explore the
feasibility of rendering fast-pass and third IM147 fast-fail decisions after earlier segments
without degrading excess emission identification. Further investigation found that
decisions could be made as early as the end of segment 2 (i.e., at Test Time =16 seconds)
if the error multiplier used in the fast-pass decision was increased during segments 2 and
3. For segment 2, the error multiplier was 3, while it was 2.5 for segment 3. Using these
criteria, average test time was reduced to 91 seconds without sacrificing any excess
emissions identification.
Development of IM147 Driver Variation Standards
As will be shown in the body of this report, previous work performed on driver variation
limits utilized Positive Kinetic Energy (PKE) limits to evaluate driver performance.
Analysis conducted for this study, however, revealed limitations with this metric, which
can result in inappropriate driver errors. As a result, a new statistic, Cumulative Positive
Power (CPP), was developed to remedy this problem. Designed to be used in
conjunction with EPA-specified absolute speed variation limits, CPP produces an
improved, more predictable, driver evaluation criteria than PKE.
When applied to the total vehicle sample for this study (minus vehicles with absolute
speed violations), the new CPP criteria produced a total abort rate of 3.4%. Since the
IM147 test, even without fast-pass, fast-fail, or retest, allows for vehicles to pass the test
after a single passing IM147, many of the driver errors in the total sample would not be
experienced since they occurred after the vehicle had already passed. Taking this into
account, the effective abort rate was 2%.
Integration of CPP Variation Limits
The CPP analysis was conducted on a subset of the 3347-vehicle population, with
absolute speed excursion violations (as defined in EPA's IM240 guidance) removed.
This resulted in an overall data set of 3006 vehicles. Using the 3006-vehicle sample, the
average test time, with the fast-pass, retest, and fast-fail criteria enabled but without the
CPP criteria applied, was 89 seconds. Once the CPP criteria were enabled, 87 tests (of
the 3006 vehicles) were extended, increasing the average test time by 1 second to 90
seconds. This resulting increase of 1.1% is less than the 2% increase projected at the end
of Section 6, which makes sense given that the 2% projection was made without the fast-
pass/fail and retest algorithms in place. The overall test time reductions caused by the
fast-pass/fail and retest algorithms would mean that fewer errors would be committed.
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Table 1-6 summarizes the change in dynamometer test time with each succeeding set of
enabled criteria. The overall impact of all the criteria is to reduce the test time by 58%,
from 217 to 90 seconds.
Table 1-6
Average Test Time (seconds)
by Test Time Reduction Methodology and CPP
Scenario
Dyno Test.
Time
Test Time
Reduction (%)
Cutpoint only, two possible
retests
217

Added fast-pass
125
42
Added retest
96
56
Added fast-fail
94
57
Allowed fast-pass at end of
Segment 2
91
58
Removed speed excursion
violations
89
59
Added CPP limits
90
58
SIP Credit Analysis
The comparison of excess emissions identification between the IM240 and IM147 that is
presented above is based on the use of CO Max standards for both test cycles. However,
to develop an estimate of the allowable SIP credit that should be allocated to the revised
IM147 CO Max standards, it is also necessary to compare excess emissions identification
between this scenario and the IM240 with EPA-recommended final cutpoints in place.
This is due to the need to establish a link to using MOBILE for SIP modeling purposes.
Configuring MOBILE with CO Max standards is not feasible; therefore, a better
approach is to run the model with final EPA standards in place and use the excess
emissions identification rates developed in this study to adjust the resulting model
outputs.
Table 1-7 shows the excess emission identification rates when the IM147 Max CO
cutpoints are compared to the IM240 final standards. Pollutant-specific identification
rates are shown both without and with the fast-pass, retest, or fail-fail algorithms enabled.
(The latter scenario includes fast-passing vehicles as early as at the end of segment 2.)
Since Arizona will be implementing the IM147 test procedure with the algorithms
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Table 1-7
Comparison of IM147 Max CO Cutpoints to IM240 Final Standards
Impact on Excess Emissions Lost*
Class
% Excess Emission Identified
(Without Fast-Pass, Retest, Fast-Fail)
% Excess Emissions Identified
(With Integrated Algorithms)
HC
CO
NOx
HC
CO
NOx
LDGV
94.1%
96.5%
85.6%
91.7%
95.6%
86.2%
LDGT1
75.6%
100.0%
79.5%
65.0%
98.1%
81.2%
LDGT2
89.0%
100.0%
53.4%
98.5%
100.0%
59.6%
Weighted
Average
86.5%
98.8%
75.1%
85.6%
97.9%
77.5%
* Percent of IM240 (Final Standards) excess emissions identified was determined using the 835 vehicle
sample.
enabled, the identification rates for this scenario are the ones that should be used to adjust
the MOBILE modeling results (based on final IM240 standards) for SIP credit purposes.
As expected, the table shows that HC and NOx identification rates are significantly lower
with the IM147 Max CO cutpoints relative to final IM240 standards. This is due to the
fact that the Max CO cutpoints are designed to maximize the CO benefits of the program
at the expense of HC and NOx benefits, while keeping maximum failure rates in each
cutpoint category to acceptable levels. The CO identification rate of 97.9% (with the
algorithms enabled) shows that the Arizona program will achieve nearly all of the
modeled benefit of the final IM240 standards. Note that this will be substantially more
effective than the current phase-in IM240 standards. The table also shows that the
addition of the integrated algorithms results in less than a 1% reduction in the excess
emissions identification rate for CO. (As noted above, the addition of the algorithms
reduce dynamometer test time from 217 to 90 seconds.)
Need for Follow-Up Analysis
As discussed above, the analysis results presented in the report are based on a relatively
small sample of IM147 and IM240 data. While the available data are significantly more
robust than the previous sample of 300 vehicles, it is clear that these results should be
revisited with a much larger sample once IM147 testing is initiated in Arizona. We
therefore recommend that as soon as one to two months of EM 147 data are collected in
the program, they should be used to verify the validity of the cutpoints and algorithms
developed in this study. This follow-up analysis would allow for any required fine-
tuning of the cutpoints and algorithms.
###
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2. INTRODUCTION
Under the Clean Air Act Amendments of 1990, metropolitan areas with the most serious
air quality problems are required to implement so-called "enhanced" I/M programs. One
element of an enhanced program is a more effective test procedure than the simple idle
tests used in "basic" I/M programs. Two different test procedures for exhaust emissions
testing in enhanced programs have been approved by EPA: the "IM240" test, and the
"Acceleration Simulation Mode" (ASM) test. Both of these procedures have been shown
to be capable of separating vehicles with excessive exhaust emissions from other
vehicles; however, the accuracy of the test depends on whether tested vehicles have been
adequately preconditioned and whether the speed-time profile associated with each test
procedure is closely followed. With either procedure, the efficiency of the testing process
depends on how quickly accurate decisions can be made as to whether a vehicle should
pass or fail.
Inadequate preconditioning of vehicles prior to testing is a potential cause of inaccurate
or inconsistent test results because exhaust emission levels depend on how thoroughly a
vehicle has been warmed up. Before the vehicle is thoroughly warmed up, high
emissions can be caused by air-fuel ratio enrichment or an inactive catalytic converter. In
addition, increased emissions due to purging of loaded canisters may also be an issue
associated with inadequate preconditioning prior to I/M testing.
Inadequate vehicle preconditioning has previously been identified as a cause of false
failures in I/M programs. Under current EPA guidance, IM240 preconditioning
procedures are woven into the "two-ways-to-pass" standards. Vehicles that exceed the
emissions standards established for the entire 239-second test are passed or failed based
on emissions occurring during the last 147 seconds of the test (also called Phase 2 or the
IM147). The separate set of standards that applies to Phase 2 is slightly more stringent.
For vehicles that initially demonstrate high emissions, the first 93 seconds (Phase 1) of
the test are used to precondition the vehicle for the second phase of the test. In addition,
EPA calls for a "second-chance" test whenever a vehicle fails the initial test by less than
50% of the standard and was in a queue for more than 20 minutes before being tested.
Previous EPA and DEO Analyses - Considerable data have already been collected
regarding the preconditioning requirements for IM240 testing. During 1996 and 1997,
Sierra conducted evaluations of this issue using data obtained from samples of vehicles
recruited from IM240 lanes in Phoenix, Arizona, and a laboratory test program at Sierra's
facilities in Sacramento. The results of the 1996 analysis were reported in SAE Paper
No. 962091.3 The 1997 evaluation also included an analysis of the effect on test duration

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of adopting EPA-recommended "final" IM240 outpoints. Preliminary conclusions from
the two evaluations are summarized below.
1.	Using the current IM240 test procedures, it is estimated that 25% of the
vehicles failing the final IM240 standards would pass with further
preconditioning.
2.	Vehicles that would benefit from further preconditioning can be identified
through modal analysis of the emissions recorded during the IM240 test.
3.	Two possible approaches to modifying the current preconditioning
procedures would be to:
a.	retain existing IM240 test procedure and two-ways-to-pass
standards, with the entire IM240 to be repeated if the Phase 2
emissions failure is marginal, emissions near the end of Phase 2 are
relatively low, or emissions during Phase 2 are significantly lower
than during Phase 1; or
b.	eliminate Phase 1 and make the initial pass/fail decision based on
running only the IM147, with a second-chance test (another
IM147) for all vehicles that initially fail, and a third-chance IM147
test if emissions during the second-chance test are significantly
lower than emissions during the initial test.
4.	Adoption of final cutpoints and more effective preconditioning procedures
involving a second fiill-IM240 (Option 3.a. above) will increase the
portion of the test involving dynamometer operation by more than 100%.
The 1997 evaluation also involved the development of improved IM240 fast-pass
cutpoints using a modal regression approach originally pioneered by the New York
Department of Environmental Conservation (NYDEC).4 This study also involved the
development of modal predictive IM240 retest algorithms designed to minimize the
fraction of vehicles either (1) identified as needing a retest when they would still fail, or
(2) not identified as needing a retest when they would have passed if retested.
As a follow-up to the 1997 evaluation for EPA, Sierra subsequently conducted an
analysis (SR98-05-01)5 for the Arizona Department of Environmental Quality (DEQ) of
the effect on failure rates, I/M program benefits, and test duration of the following
changes to the current IM240 procedure: (1) implementation of the Option 3.b
preconditioning procedures summarized above; (2) adoption of interim ("Max CO")
cutpoints designed to maximize the carbon monoxide emission reduction benefits being
achieved by the program; and (3) the exemption of either the first four or first five model
years from program requirements.
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To analyze the IM147-only preconditioning option, Sierra used a combination of data
from the 2% random test sample (consisting entirely of full-duration tests) that is
routinely collected in the Arizona IM240 program and a limited number (101 tests) of
triplicate (back-to-back-to-back) IM147 tests that were conducted as part of the 1997
EPA evaluation. A key element of the analysis methodology involved the development
of "Phase 2b" cutpoints to complement the full IM147-only cutpoints. (The Phase 2b
cutpoints were applied in a manner similar to the current IM240 procedure in which
vehicles passing in Phase 2 are considered passing for the entire test.) Fast-pass cutpoints
for both the entire IM147 and Phase 2b were also developed.
As noted above, while the 1997 EPA study involved the analysis of a considerable
amount of IM240 data, only a small subset (101 vehicles) was of use in projecting credit
levels and test times for an IM147 test program. An additional concern is that the 101-
vehicle study was not specifically designed to determine excess emission identification
rates and SIP credit levels. As a result, EPA issued a follow-up work assignment to
Sierra in 1998 that involved the collection of test data from triplicate IM147 tests
followed immediately by a full-duration IM240. Data were collected from 304 randomly
selected light-duty cars and trucks arriving at the test lane during normal queuing
conditions. These data were then analyzed to verify the preliminary excess emission
identification rates and average test time estimates projected for the Arizona IM program
from the Phase 2 and IM240 data sets collected in previous studies.
As part of the 1998 study for EPA, improved fast-pass and retest algorithms for the
IM147 were developed using the same approach used previously in developing similar
IM240 algorithms; however, the cutpoint scenarios evaluated in the study did not include
the Max CO cutpoints previously developed for DEQ. Given Arizona's need for the
maximum feasible CO reductions from its I/M program, DEQ has decided to implement
this set of cutpoints. A follow-up study is therefore needed to develop improved fast-pass
and retest algorithms for the Max CO IM147 cutpoints. Gordon-Darby collected
additional test data from roughly 3,000 vehicles in the Phoenix area that can be used in
this analysis. Of these vehicles, approximately 2,500 received triplicate IM147s only; the
remaining 500 vehicles will receive triplicate IM147s followed by a single full-duration
IM240.
PKE Speed Variation Criteria - An additional IM147 implementation issue was the lack
of allowable speed variation criteria for the shortened drive trace. In addition to the false
failures caused by inadequate preconditioning, inadequate control over vehicle operation
during the IM240 test procedure can contribute to inaccurate results. The ability of a
driver to follow the IM240 speed-time trace has a significant effect on the emissions
recorded during the test. To limit this variation in test results, tolerances are applied to
driver performance.
In the 1997 IM240-related evaluation for EPA (SR98-02-01),2 Sierra developed improved
speed variation criteria based on the total Positive Kinetic Energy (PKE) change per mile
traveled during the IM240 cycle. These criteria were designed to minimize the variation
in emissions while still being feasible for use by minimally trained drivers with a
reasonable aptitude for dynamometer driving. However, only IM240 drive cycle criteria
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were developed in the 1997 study. Therefore, further analysis was needed to develop
similar speed variation criteria for the IM147.
Scope
To address the issue of inadequate preconditioning without compromising test throughput
excessively, Arizona decided to change its test procedure to the IM147 beginning
January 1, 2000. This program upgrade will also include implementation of the Max CO
cutpoints previously developed by Sierra, which are designed to maximize the CO
benefits of the program.
To aid in this implementation effort, EPA issued a work assignment (#1-08) to Siena to
develop the necessary test criteria. Under Work Assignment 1-08, Sierra is to complete
the following tasks:
1.	Develop projected IM147 failure rates for the Arizona I/M program using
the 3,000-vehicle data set currently being collected by Gordon-Darby.
This evaluation is to include start-up, midpoint, Max CO, and final IM147
cutpoint scenarios.
2.	Develop modal IM147 fast-pass standards for the Max CO cutpoints using
(a) the modal regression technique used in the 1998 EPA study to develop
IM147 fast-pass standards, and (b) the 3,000-vehicle data set and the 304-
vehicle data set collected in 1998.
3.	Develop modal predictive IM147 retest algorithms for the Max CO
cutpoints using (a) the same technique used in the 1998 EPA study to
develop ,IM 147 retest algorithms, and (b) the 3,000-vehicle data set and
the 304-vehicle data set collected in 1998.
4.	Develop modal IM147 fast-fail criteria that can be used to terminate
retests if emissions performance is not improving during the retest.
5.	Develop fast-pass and full-duration PKE criteria for the IM147 start-up,
midpoint, Max CO, and final cutpoints using the 3,000-vehicle and 304-
vehicle data sets, as well as the 16,581-vehicle data set from the 1997
study of IM240 PKE limits for EPA.
Seven different tasks were proposed to accomplish these objectives.
Task 1. Test Plan Development and Data Collection Assistance - This task covered
working with Gordon-Darby in its efforts to collect the test data needed to complete the
remaining tasks. Data collection, driver participation incentives, and other program-
related details were performed under the guidance of DEQ and Gordon-Darby and were
not Sierra's responsibility. Sierra provided assistance on an as-needed basis to resolve
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any problems or questions (e.g., regarding test protocols, data record format, etc.) that
developed during the data collection process in Arizona.
Task 2. Failure Rates - After completion of the vehicle testing described in Task 1, Sierra
analyzed the resulting data. Using the same approach as utilized in the previous EPA and
DEQ studies, projected IM147 failure rates were developed for both passenger cars and
light-duty trucks. Separate projections were generated for the start-up, midpoint, Max
CO, and final IM147 cutpoints developed under the previous analyses.
Task 3. Fast-Pass Outpoints - Data obtained in Task 1 as well as the 304-vehicle IM147
data set collected in 1998 were analyzed to develop fast-pass cutpoints and algorithms
associated with the Max CO cutpoints. The same approach previously used to develop
fast-pass cutpoints for the other cutpoint scenarios (i.e., start-up, midpoint, and final) was
followed in this analysis.
Sierra developed fast-pass cutpoints and algorithms for the Max CO cutpoints, with
minor adjustments to the model year groups when the data indicated that such a change
improved accurate emission identification. Separate sets of fast-pass cutpoints were
developed for these new model year groups and vehicle classes as contained in EPA's
IM240 test guidance.
The impact of the resulting fast-pass cutpoints on average dynamometer test time and
excess emissions identified was evaluated using the same techniques as in the previous
analyses. Excess emissions identified will be expressed as the percent of excess
emissions that are identified relative to those identified on the IM240 test.
Task 4. Retest Algorithms - The same data used in Task 3 were analyzed to develop retest
algorithms associated with the Max CO cutpoints. While this task originally charged
Sierra with utilizing the same approach previously used to develop retest algorithms for
the other cutpoint scenarios (i.e., start-up, midpoint, and final), subsequent comments
from Gordon-Darby resulted in an alternate retest algorithms. The impact of the resulting
retest algorithms on average dynamometer test time was evaluated using the same
technique as in the previous analyses.
Task 5. Fast-Fail Criteria - The same data used in Task 3 were analyzed to develop
criteria for evaluating mid-test emissions during IM147s in order to determine whether
emissions performance is improving during the retest. The resulting criteria were
structured to "fast fail" vehicles that are not benefitting from such retesting. The impact
of the resulting fast-fail criteria on average dynamometer test time was evaluated Using
the same technique as in the previous analyses.
Task 6. Driver Variation Criteria - The same data as used in Task 3 were analyzed to
develop fast-pass and full duration driver variation limits for the IM147 start-up,

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midpoint, Max CO and final outpoints.* The analytical approach used in the 1997
analysis was initially followed; however, subsequent results led to the development of an
improved variation metric, Cumulative Positive Power (CPP). Consistent with the
approach used in the previous analysis, the evaluation was structured to develop CPP
limits designed to keep the effective abort rate due to drive trace violations to less than
3%. The impact of the resulting criteria on average dynamometer test time was also
evaluated.
Organization of the Report
Following this introduction, Section 3 describes data collection and the data sets used
throughout this study. Section 4 explains the cutpoint analysis including the revision of
the Max CO cutpoints using the larger data sample; it also details failure rates for the
Startup, Intermediate, Final, and Max CO standards. Section 5 describes the optimized
fast-pass and retest criteria. In addition, it details the new fast-fail algorithm and criteria
as well as the net results of optimized criteria when run simultaneously. Section 6
describes the new CPP driver variation limits and Section 7 integrates the CPP limits with
the optimized IM147 to show the net effect on test time. Section 8 lists the references
cited in the report.
###
The original workplan called for the 16,581-vehicle data set from Sierra's 1997 PKE study for EPA
(SR98-02-01) to also be used in this latest analysis. However, as discussed in more detail in subsequent
sections, a "time realignment" (of emissions versus vehicle speed) was incorporated into the analysis. This
made use of the previous data more problematic. It was determined that the effort required to adjust these
data could not be justified in terms of a significant increase in the accuracy of the results; therefore, a
decision was made not to use these previous data.
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3. TEST DATA
Data used in this study are divided into three distinct groups consisting of 304, 543, and
2518 vehicles. Vehicles in each of the groups were given three consecutive IM147 tests
regardless of the result.
The 304-vehicle sample was collected by Gordon Darby for Task 1 of Work Assignment
SR99-10-021 at the Gordon Darby I/M lanes in Phoenix, Arizona, during March 1998.
The data included 193 cars and 111 light-duty trucks tested over triplicate IM147 tests
followed by a full IM240 test as illustrated in Figure 3-1.
Figure 3-1
Test Sequence Used to Investigate Triplicate IM147 Tests in Arizona Test Lanes
60
50 -
Z" 40 H
a.
E
"O 30 
o
a>
 20 i
10 -
5-15 minutes First
in queue IM147 test
Second	Third
IM147 test IM147 test
Time
Hot
IM240 Test
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For this set, the study inspector would select vehicles by scanning the queue for the
closest white vehicle waiting in the lanes. If there were no white vehicles in the queue,
the inspector would look for the palest, closest vehicle waiting in the lanes. The inspector
approached the first 1981 or newer vehicle following that vehicle, checked to make sure
the vehicle had at least half a tank of gas, and asked the vehicle owner if he or she was
interested in participating in a study that would take approximately 30 minutes, for a
payment of $50. The selection process resulted in vehicles waiting in a queue for
approximately 5 to 15 minutes prior to testing. Most of the vehicles participating in the
program were receiving their initial test; however, 12 vehicles in the database were being
re-tested after an initial failing score.
As discussed in SR99-10-02, four vehicles were pulled out of the original 304 vehicle
data set due to anomalous results. For this study, while Sierra did find some vehicles with
anomalous results in the newer data samples (e.g., passing the initial IM147 yet grossly
failing the final one), these vehicles were not removed from the sample, with the data set
instead being viewed as representative of the in-use fleet. To remain consistent regarding
the treatment of data from the older sample, however, the same four vehicles were
removed for this analysis. The four vehicles are described below.
	Record 14, a 1988 Pontiac Bonneville, had relatively low CO emissions
during the first and second EM147 test (1.35 and 3.42 g/mi, respectively).
However, CO emissions during the third IM147 increased substantially (to
55.72 g/mi) and were higher still during the IM240 following the IM147
testing. It is interesting to note that CO was emitted in measurable
quantities throughout the test, and the large increases are not attributable
to a specific section of the trace. It thus appears that the gradual emissions
increase could be attributable to excessive purge as the vehicle warmed up
or to some kind of catalyst protection scheme.
	Record 15, a 1989 Dodge Dynasty, had moderate CO emissions during the
first three IM147 tests (14 to 18 g/mi), but emissions during the IM240
test were excessive, particularly during the end portion of that test
(106 g/mi). Reviewing the modal CO emissions in Figure 3-5, one
observes that the vehicle appears to go into open-loop operation at the start
of the large hill of the end portion of the test (i.e., beginning at about
second 160 of the IM240). Although CO emissions accrue throughout this
test, the period from 160 to 230 comprises the bulk of the emissions.
	Record 23, a 1993 Ford Ranger, shows a very similar emissions response
throughout the three IM147 tests. As seen in Figure 3-6, most of the CO
emissions occur during seconds 62 to 75 of the IM147. During the end
portion of the IM240, a similar pattern is observed. In that test, however,
substantial CO is also emitted during the high-speed portion of the trace.
It is not entirely clear what has caused this, but it appears that the vehicle
did not follow the speed-time trace as smoothly during the end portion of
the IM240 as it did during the first three IM147 tests.
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Record 24, a 1995 Toyota 4Runner, had decreasing emissions throughout
the first three IM147 tests, emitting only 0.72 g/mi CO during the third
IM147.
After these vehicles were removed, the remaining sample from the original data set
consists of 191 cars and 109 trucks for a total of 300 vehicles.
The 2518- and 543-vehicle data sets were collected for this work assignment by Gordon
Darby at the ten I/M lanes in Phoenix, Arizona during June, July, and August 1999. Like
the original data set, these tests were conducted at the I/M lanes in Phoenix, Arizona.
Unlike the original data set, however, all motorists were asked to participate in the study,
rather than simply those following a white-colored vehicle in the queue. The exception to
this occurred toward the end of the testing when Gordon Darby staff, based on direction
from Sierra, targeted certain vehicle model years and vehicle types to ensure that these
groups were adequately represented in the test data.
The main difference between the 2518- and 543-vehicle samples was administration of
the IM240 test at the conclusion of the IM147 test. For the 2518-vehicle sample, only
failing vehicles were given the IM240 test. In the 543-vehicle sample, all vehicles were
given the IM240 test regardless of their IM147 result.
Motorists were not required to participate in this testing. To encourage motorists to
participate, inspection fees were waived for these tests. Inspection fees amount to S25
per inspection. Statistics detailing the number of refusals were not kept.
As previously mentioned, anomalous vehicle test data were not thrown out of the latter
two samples. There was, however, one vehicle identification number (VIN), "123456,"
that appeared multiple times with different vehicles. Gordon-Darby staff confirmed that
this was a test VIN and should be excluded from analysis, which was done. Once this
VIN was removed, the larger sample comprised 2512 vehicles (1360 cars and 1152
trucks) while the smaller sample comprised 535 vehicles (229 cars and 306 trucks).
The model year distribution for each of the samples is shown in Figure 3-2. The YTD
(year-to-date) October 1999 line represents the initial test for 2% random sample vehicles
tested in 1999 through October. Anomalous vehicles have been removed.
For the most part, the model year distribution of the data samples mirrors the random
sample distribution reasonably well. One notable exception can be seen with newer
model year vehicles for the 300-vehicle sample. When that data set was collected, model
year exemptions for the five newest model years were not in place. As a result, this
sample has greater representation throughout these years. The newer data sets were
collected with the model year exemptions in place; therefore, they follow the 1999 YTD
random sample more closely.
One important difference in this study versus previous studies was how time-alignment
was handled. For the previous studies, individual channels (HC, CO, etc.) were aligned
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Figure 3-2
Model Year Distribution of
Sample Fleet and AZ Overall Fleet
20% 	:	:	
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
Model Year
2512 Vehicle Sample Y//A 535 Vehicle Sample tss&l 300 Vehicle Sample -YTD October 1999
according to their T90 response times. The T90 response time is the length of time
necessary for the analyzer to see 90% of a positive step change in the gas concentration
that was introduced at the exhaust collection cone. By correcting for response time,
emission events can be linked to the corresponding drive trace event.
The justification for T90 time-alignment centered on the response curve of the gas bench.
Typically, the response curve for a bench will appear somewhat asymptotic; as the
measured gas value closes in on the actual value, the absolute rate at which the measured
value approaches the target value decreases. As a result, the T90 time, which is relatively
short, becomes a good approximation to time an event.
Unfortunately, the response time measurement of the analyzer is composed of two
elements, gas bench response time and transport time, which is the amount of time
necessary for the CVS (Constant Volume Sample) blower to transport the sample from
the collection cone to the gas analyzer bench. This second response time element,
transport time, obscures the gas bench response curve through gas mixing that occurs
during transport.
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Noting this effect, Gordon-Darby staff, after reviewing the raw data, suggested that the
T90 time alignment overcompensated and thus caused short-duration emission events
during a transient cycle to appear to precede the triggering speed event. Given the
interaction of the transport time with the bench response time, they suggested that a more
appropriate alignment measure is T50 response time, which would be more conservative
and alleviate the aforementioned problem.
To accommodate this change, all the existing data, which were previously aligned using
T90 alignment by Gordon Darby, had to be realigned to T50 response times. This
included data collected specifically for this study. Per Gordon-Darby staff, this change
required shifting data for each of the channels (HC, CO, NOx, and C02) four seconds
later relative to the speed signal. This was accomplished by adding four seconds of data
to the front of each test, in which it was assumed that the modal emissions for the entire
period were identical to those measured during the "previous first second" (i.e., now
second 5) of the test. This assumption is considered reasonable since the vehicle is at idle
during this entire period.
IIIIII
###
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4. IM147 CUTPOINT ANALYSIS
This section of the report discusses the application of the IM147 Max CO cutpoints to the
3347-vehicle data set. Divided into three parts, the section first discusses changes in
overall IM147 test time. The second part of the section addresses revising the Max CO
cutpoints originally developed in SR99-10-02" based on the large data set now available.
The third and final part of the section details the failure rate when these revised cutpoints
are applied to the 3347-vehicle data set.
IM147 Test Length
Sierra originally developed the IM147 test cycle based on the last 147 seconds of data
from the IM240 drive trace. Following the precedence EPA established with the IM240
drive trace, this meant that the modal results of the IM147 test would have 147 seconds of
data. In short, there were no constraints regarding an odd versus even number of seconds
in the overall test cycle, nor in Phase 2 of the test cycle.
To simplify implementation of the IM147 test in Arizona, Gordon Darby, and DEQ
agreed that modal data would be recorded once every two seconds instead of second-by-
second as is done with the IM240. This allows the IM147 cycle data to fit into the same
size record format as is currently used for full duration IM240 tests. As a result, having
an odd number of seconds in the drive trace creates a problem of what to do with the odd
second.
To alleviate the problem, Gordon Darby suggested that the speed/time trace define the
boundaries for the test time instead of the actual number of data points reported. In other
words, assuming the first speed/time point is labeled zero seconds, the first modal data
result would be recorded for second 1 and the last for second 146. Phase 2 of the test
would also be revised to start at second 66 (first data reported for second 67) and extend
to the end of the test. The net effect of both of these changes is that both the composite
results and the Phase 2 results will contain an even number of seconds. Since this
addresses the issue of Gordon-Darby's two-second average data collection with no
apparent negative consequences, Sierra revised the test length accordingly. Additional
information on the drive trace is presented in Section 5.
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Figure 4-1
IM147 Trace
Phase 1 and Phase 2
Time (sec)
Cutpoint Analysis
Task 1 of the work assignment required projecting failure rates for the Arizona I/M
program using the combined (3,347 vehicles) data set collected by Gordon Darby. The
projection includes four sets of emission cutpoints: start-up, midpoint, Max CO, and final
for the IM147 test. Since the original Max CO cutpoints were developed using the 304-
vehicle sample compiled for SR99-10-02, Sierra first revisited these cutpoints and model
year groupings to ensure their accuracy against the larger data set.
Accordingly, the model year groupings were modified to avoid anomalously high or low
failure rates for any individual model year/vehicle type combination. A second objective
in establishing the endpoints of the model year groupings was to ensure that changes in
emissions control technology were properly reflected in the various model year
groupings. (There is an obvious and direct relationship between the control technology
installed on a vehicle and its ability to comply with a given set of cutpoints.)
Once the model year groupings were revised, the IM147 Max CO cutpoints were
developed by regressing the final emissions results from the IM240 test against the final
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emission results from the third IM147 test for each of the three exhaust constituents. For
this regression, Sierra used only vehicles from the two of the three data sets where
vehicles were automatically given an IM240 test regardless of their IM147 test results
(the 300- and 535-vehicle samples). Because the third data set contained IM240 data
only for vehicles failing the IM147, this data set would have created a regression bias and
was therefore excluded from this part of the analysis. The resulting regression equations
were then used to extrapolate IM147 composite cutpoints from the IM240 composite
cutpoints developed previously in SR99-10-02. Three linear regression equations were
developed for each of three model year groupings. Equation 4-1 illustrates the
regression equation.
IM147 Cutpoint = slope * (IM240 Cutpoint) + Intercept	[4-1]
Table 4-1 details the appropriate model year/emission constituent regression coefficients
to be used in the above equation. The model year groupings identified in this table are
not the same as the model year groupings used in the emission cutpoint tables, which
vary depending upon vehicle type.
Table 4-1
IM240 to IM147 Composite Regression Equation Coefficients
Model Year Group
Emission
Constituent
Slope
Intercept

HC
0.896629
0.110694
1981-1985
CO
1.020463
0.858255

NOx
1.065128
0.085613

HC
0.933646
0.056509
1986-1989
CO
0.939067
1.679632

NOx
1.077932
0.058971

HC
0.963839
0.026672
1990+
CO
1.037836
0.392486

NOx
1.102698
0.048771
Phase 2 cutpoints were developed by regressing the IM147 composite test scores against
the IM147 Phase 2 scores. Unlike the composite score regressions, however, the
equations had to be adjusted to preserve the relationship between composite versus
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Phase 2 scores existing in the IM240 test. IM240 Phase 2 outpoints are more rigorous
than the composite cutpoints, presumably to minimize falsely passing vehicles. After
studying the IM147 data, a multiplier of 0.9 was used with the regression since it
provided additional defense against false failures while maintaining the possibility of a
Phase 2 pass. The following Phase 2 regression equation (4-2) was used to extrapolate
IM147 Phase 2 cutpoints from the IM147 composite cutpoints.
IM147 Phase 2 - 0.9 * (Slope * (IM147 Composite) + Intercept) [4-2]
Table 4-2 details the appropriate regression coefficients to be used with the above
equation. Unlike the composite regression coefficients shown in Table 4-1, IM147
composite to Phase 2 coefficients were held constant across model years..

Table 4-2

IM147 Composite to Phase 2 Regression Equation Coefficients
Emission Constituent
Slope
Intercept
HC
0.807408
0.012886
CO
0.881965
-0.569281
NOx
0.989412
-0.083696
Table 4-3 shows the revised IM147 Max CO cutpoints developed using both sets of
regression equations. In two places, the revised NOx cutpoints seem anomalous because
they are actually less stringent for newer vehicles (1989 to 1990 LDGV and 1987 to 1988
LDGT2). As it turns out, these anomalies occurred at or near the regression equation
breakpoints.* The slight discontinuity caused by this change created the apparent
anomaly.
To apply the regression equations to the model year groupings, which vary depending upon the vehicle
type, cutpoints for some model years were determined using the adjacent regression equation.
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Table 4-3
Revised IM147 Max CO Cutpoints
(Composite/Phase 2)
Vehicle Class Model Years HC CO NOx
LDGV
1981-82
2.80/2.05
26.37/20.42
3.28/2.85
1983-85
2.08/1.53
17.19/13.13
3.28/2.85
1986-89
1.46/1.07
15.77/12.00
2.75/2.38
1990-95
0.99/0.73
12.85/9.68
. 2.81/2.42
1996+
0.80/0.59
12.85/9.68
2.25/1.93
LDGT1
1981-85
3.70/2.70
31.47/24.47
5.41/4.74
1986-89
2.86/2.09
25.16/19.46
4.91/4.30
1990-95
1.95/1.43
21.15/16.28
4.46/3.90
1996+
1.57/1.15
21.15/16.28
3.36/2.91
LDGT2
1981-85
4.06/2.96
51.88/40.67
6.48/5.69
1986-87
3.79/2.77
39.24/30.64
5.99/5.26
1988-95
2.92/2.13
26.34/20.39
6.11/5.37
1996+
2.34/1.71
26.34/20.39
4.46/3.90
Comparison of Failure Rates
After revising the Max CO cutpoints using the 835-vehicle sample, the failure rates were
evaluated using each of the four sets of IM147 cutpoints (Startup, Intermediate, Final, and
Max CO) and the combined vehicle data set (3347 vehicles). While the IM147 Max CO
cutpoints were revised for this study, the IM147 Startup, Intermediate, and Final
cutpoints were developed as part of SR99-10-02 and are shown in Appendix A.
Table 4-4 shows how the failure rate changed with the different cutpoints for the third
and final IM147. The overall failure rate will be slightly less when actually implemented
since some vehicles will pass and therefore complete the test after an earlier IM147 even
though they would go on to the fail the third one if the test was continued. More detailed
information on the failure rates is provided in Appendix B.
This table helps to clarify the relationship between the four sets of cutpoints. The
Startup, Intermediate, and Final cutpoints result in nearly equal HC, CO, and NOx failure
rates, which increase with increasing cutpoint stringency. The Max CO standards, when
compared to the final standards, reduce the HC and NOx failure rates, and instead
increase the CO failures, especially for light-duty trucks.
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Table 4-4
Failure Rates, Third IM147
Vehicle
Type
HC
CO
NOx
OVERALL
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Max CO Cutpoints
LDGV
114
1666
6.4%
250
1530
14.0%
155
1625
8.7%
390
1390
21.9%
LDGT1
49
915
5.1%
156
808
16.2%
70
894
7.3%
219
745
22.7%
LDGT2
25
578
4.1%
63
540
10.4%
30
573
5.0%
93
510
15.4%
All
188
3159
5.6%
469
2878
14.0%
255
3092
7.6%
702
2645
21.0%
Startup Cutpoints
LDGV
106
1674
6.0%
130
1650
7.3%
141
.1639
7.9%
281
1499
15.8%
LDGT1
52
912
5.4%
33
931
3.4%
48
916
5.0%
108
856
11.2%
LDGT2
33
570
5.5%
30
573
5.0%
28
575
4.6%
67
536
11.1%
All
191
3156
5.7%
193
3154
5.8%
217
3130
6.5%
456
2891
13.6%
Intermediate Cutpoints
LDGV
157
1623
8.8%
161
1619
9.0%
196
1584
11.0%
367
1413
20.6%
LDGT1
80
884
8.3%
53
911
5.5%
73
891
7.6%
165
799
17.1%
LDGT2
40
563
6.6%
42
561
7.0%
42
561
7.0%
93
510
15.4%
All
277
3070
8.3%
256
3091
7.6%
311
3036
9.3%
625
2722
18.7%
Final Cutpoints
LDGV
280
1500
15.7%
240
1540
13.5%
277
1503
15.6%
507
1273
28.5%
LDGT1
120
844
12.4%
85
879
8.8%
127
837
13.2%
239
725
24.8%
LDGT2
73
530
12.1%
56
547
9.3%
82
521
13.6%
149
454
24.7%
All
473
2874
14.1%
381
2966
11.4%
486
2861
14.5%
895
2452
26.7%
Figures 4-2 through 4-5 show how the failure rate changes as vehicles progress through
the three IM147 tests. Since most of the increase in failures occurs between the first and
second IM147 tests, it is reasonable to expect that algorithms designed to shorten the test
would end the test prior to the third IM147. As will be shown later in the report, this is
indeed the case. Once the fast-pass, retest, and fast-fail criteria are applied, only 162
vehicles out of 3347 (4.8%) are tested beyond the second IM147.
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Figure 4-2
Failure Rate by Consecutive IM147
Max CO Cutpoints
HC Failure Rate by Vehicle Type
u
i
~	1st IM 147
 2nd IM 147
~	3rd IM 147
PC
LDT I LDT2
Vehicle Type
All
NOx Failure Rate by Vehicle Type
o
z
~	1st IM 147
El 2nd IM 147
~	3rd IM 147
PC
LDTI LDT2
Vehicle Type
All
CO Failure Rate by Vehicle Type
o
<_>
~	1st IM 147
H 2nd IM147
~	3rd IM 147
PC
LDTI LDT 2
Vehicle Type
All
Overall Failure Rate by Vehicle Type
3 5.0%
~ 1st IM 147
B 2nd IM147
03rd IM 147
PC
LDTI LDT2
Vehicle Type
All

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Figure 4-3
Failure Rate by Consecutive IM147
Startup Outpoints
HC Failure Rate by Vehicle Type
u
X
PC
LDTI LDT2
Vehicle Type
All
~ I st IM 14 7
H2nd IM 147
03rd IM 147
NOxFailure Rate by Vehicle Type
o
2
LDTI LDT2
Vehicle Type
~ Isl IM 147
12nd IM 147
133rd IM 147
o
o
CO Failure Rale by Vehicle Type
PC	LDT I LDT2	All
Vehicle Type
~ Isl IM 147
B2nd IM 147
03rd IM 147
Overall Failure Rate by Vehicle Type
25.0%
PC
LDTI	LDT2
Vehicle Type
All
~ Isl IM I 47
 2nd IM 147
03rd IM 147

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Figure 4-4
Failure Rate by Consecutive IM147
Intermediate Cutpoints
HC Failure Rate by Vehicle Type
~ lst IM 147
S3 2nd IM 147
03rd IM 147
PC	LDT1 LDT2	All
Vehicle Type
NOx Failure Rate by Vehicle Type
o
z
O I st IM 147
E 2nd IM147
03rd IM 147
PC
LDT1 LDT2
Vehicle Type
All
CO Failure Rate by Vehicle Type
~	1st IM 147
12nd IM147
~	3rd IM 147
PC
LDTI LDT2
Vehicle Type
All
Overall Failure Rate by Vehicle Type
~ 1st IM 147
 2nd IM 147
03rd IM 147
PC
LDT1 LDT2
Vehicle Type
All

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Figure 4-5
Failure Rate by Consecutive IM147
Final Outpoints
HC Failure Rate by Vehicle Type
~ 1st IM 147
El 2nd IM I 47
03rd IM 147
PC
LDTI LDT2
Vehicle Type
All
NOx Failure Rate by Vehicle Type
2 5.0%
03 1st IM 147
Q2nd IM 147
03rd IM 147
PC
LDTI LDT2
Vehicle Type
All
CO Failure Rate by Vehicle Type
~ isi IM 147
12nd IM147
03rd IM 147
PC	LDTI LDT2	All
Vehicle Type
Overall Failure Rate by Vehicle Type
~ 1st IM 147
|2nd IM147
03rd IM 147
PC
LDTI LDT2
Vehicle Type
All

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5. OPTIMIZED IM147 TEST CRITERIA
This section presents the analysis methodology and results used to develop optimized
IM147 test criteria. Criteria developed in the study include fast-pass standards, retest
algorithms, and fast-fail criteria. As decribed below, each set criteria was evaluated in
turn to determine its overall effect on test time and excess emissions identification.
Modal Fast-Pass Standards
IM147 fast-pass standards were originally developed for startup, midpoint, and final
standards as part of SR99-10-02.1 The current study furthered that work by developing
fast-pass standards for the Max CO cutpoints. The fast-pass regression coefficients
determined for both SR99-10-02 and the current study were developed using the
methodology described in Sierra Report No. SR98-02-01,2 "Additional Study of
Preconditioning Effects and Other IM140 Testing Issues." As detailed in that report, the
selected drive trace is divided into segments over which mass emissions for HC, CO, and
NOx are summed. By performing a multivariate linear regression of the modal mass
emissions against the composite emissions result, we can determine the coefficients
needed to predict final emissions at mode ends throughout the test. Equation 5-1
illustrates how these coefficients are used to predict emissions.
rt
P240 = Cn + ^ {Snm X Xnmj + ( Me X En)	T5-11
m= 1	L
Predicted emissions after completing n segments
Regression intercept for equation n
Regression coefficient for segment m in equation n
Total emissions over a given segment m in equation n
Error multiplier (usually 2 unless otherwise specified)
Error in regression equation n
Equation number (corresponds to the number of modal
segments completed)
Segment number
After reviewing the proposed fast-pass and retest procedures described in SR99-10-02,
Gordon-Darby staff expressed several concerns regarding actual in-use implementation of
that procedure. In response, the workplan developed for the current project indicated that
Where:	P240n
Cn
Snm
Xnm
Me
En
n
m
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Sierra would initiate further discussions with Gordon-Darby and evaluate possible
changes to the previously developed procedures to address these concerns. Specific
concerns that were voiced by Gordon-Darby staff include the following:
1.	Insufficient number of fast-pass segments - SR99-10-02 divides
the IM147 drive trace into 14 segments. While the segments in the
first half of the test usually comprise 8 to 10 seconds, several
segments in Phase 2 of the drive trace are considerably longer (up
to 19 seconds in duration). Gordon-Darby expressed concern that
extended segments may force vehicles to be tested longer than
necessary prior to a fast-pass.
2.	Fast-pass segments containing an odd number of seconds - To allow the
resulting modal test data up to three possible IM147 cycles to fit into the
same size record format as currently used for full duration IM240 tests.
Gordon-Darby plans to record two-second averages rather than once every
second as is presently done. To simplify its lane software, Gordon-Darby
requested that Sierra realign the segments to agree with the planned
frequency of data storage.
3.	Nonalignment of fast-pass segments with proposed retest modes - To
further simplify its programming process, Gordon-Darby asked that Sierra
try to coincide retest mode breaks with segment breaks.
In addition to simply developing updated fast-pass regression coefficients and retest
algorithms based on an expanded data set, this study addresses the above concerns. In
response to the first two concerns as well as the test length issue (odd vs. even number or
seconds), the fast-pass segments have been revised as shown in Table 5-1.
As shown in the table, there are now 20 segments ranging from 4 to 10 seconds in
duration. Each segment contains an even number of seconds. Phase 2 of the IM147 now
begins at segment 11 and extends through the end of the test. The test includes 146
seconds of data (collected starting at second 0 and ending at second 146).
When the IM240 segments were originally created as part of SR98-02-01, they were
divided in such a way that segments characterized different modes of vehicle operation.
Some segments were composed of hard accelerations, while others characterize cruises,
and everything between, including decelerations. While this study re-aligned the
segments with deference to ensuring 4- to 10-second segment lengths divided along even
increments, care was taken to preserve the original intent of the segmentation. Figure 5-1
shows the re-aligned segments positioned against the drive trace.
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Table 5-1
Revised IM147 Segments
Segment Initial Second Data Final Second Data
1
1
4
2
5
16
3
17
22
4
23
28
5
29
34
6
35
42
7
43
48
8
49
54
9
55
60
10
61
66
11
67
76
12
77
82
13
83
92
14
93
98
15
99
108
16
109
112
17
113
116

117
122
19
123
132
20
133
146
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Figure 5-1
IM147 Test Segments Used for Fast-Pass Cutpoint Development
Time (sec)
Using these revised test modes, Sierra applied the aforementioned regression method to
the 3,347-vehicle sample supplied by Gordon-Darby. Thirty-nine distinct regression
models, shown in Appendix C, were developed from these data. Models were created for
each vehicle type (LDGV, LDGT1, and LDGT2), emission constituent (HC, CO, and
NOx), and phase (composite and Phase 2 only). There are 20 equations for the composite
models and 10 equations for the Phase 2 models.
After revising the regression models, they were applied to the 835-vehicle sample
(unbiased IM240 sample) to determine how the models would affect excess emissions
identified by the IM147 test. Excess emissions are defined as emissions collected during
an IM240 test in excess of the applicable standard for a given vehicle. The IM147 test
receives credit for identifying excess emissions if it fails a vehicle that had excess IM240
emissions. Table 5-2 shows the excess emissions versus test time results when the
Fast-Pass algorithm was enabled. Max CO standards were used for both the IM147 and
the IM240.
As Table 5-2 shows, the IM147 test with the fast-pass criteria enabled still identifies over
91% of the excess emissions for each of the three exhaust constituents and still
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Table 5-2
Modeled Fast-Pass Results
Excess Emissions vs. Average Test Time

No Fast-Pass
Fast-Pass Enabled
Excess HC Identified
94.3%
92.7%
Excess CO Identified
98.2%
94.3%
Excess NOx Identified
92.1%
91.2%
Average Test Time
217 seconds
125 seconds
significantly reduces average test time.* Without fast-pass enabled, the pass/fail
evaluation occurs only after each IM147 is complete. If the vehicle passes the IM147, the
test would be complete at that point. If the vehicle fails, another IM147 would be run,
with up to three IM147s conducted on any one vehicle. If the vehicle is still failing at the
end of the third IM147, it fails the overall test. Since each IM147 lasts 146 seconds, the
maximum time a vehicle could be tested is 438 seconds.
Without fast-pass, the average test time for the 3,347-vehicle sample was 217 seconds.
Once the fast-pass algorithm was applied, the average test time dropped to 125 seconds,
for a reduction of 92 seconds (42%). This is similar to the test time estimate of 121
seconds shown in SR99-10-02' for the final standards with fast-pass.
Table 5-3 details excess emissions identification by vehicle type and model year groups.
Appendix D provides additional excess emission analysis results. As will be shown later
in the report, excess emissions identified increase with the addition of retest and fast-fail
algorithms.
Average test time refers to the estimated time for that portion of the test in which the vehicle is driven on
the dynamometer.
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Table 5-3
Excess Emissions Identified3
(With and Without Fast-Pass)
Vehicle
Class
Model
Year
No Fast-Pass
Fast-Pass Enabled
Excess HC
Identified
Excess CO
Identified
Excess NOx
Identified
Excess HC
Identified
Excess CO
Identified
Excess NOx
Identified
LDGV
81-82
75.71%
99.62%
98.42%
N/A
87.6%
100.0%
83-85
100.00%
98.83%
71.31%
100.0%
97.4%
100.0%
86-89
100.00%
0.00%
100.00%
99.3%
97.5%
100.0%
90-95
N/A
N/A
100.00%
96.9%
85.3%
91.4%
96+
84.26%
99.10%
95.82%
N/A
N/A
N/A
ALL
92.64%
100.00%
85.50%
98.6%
93.9%
97.6%
LDGT1
81-85
100.00%
100.00%
56.20%
75.7%
92.6%
98.4%
88-89
100.00%
100.00%
55.88%
92.4%
91.0%
71.3%
90-95
N/A
N/A
N/A
0.0%
0.0%
100.0%
96+
95.61%
100.00%
73.26%
N/A
N/A
0.0%
ALL
. N/A
87.65%
100.00%
79.3%
91.7%
94.0%
LDGT2
81-85
100.00%
97.44%
100.00%
92.6%
100.0%
85.5%
86-87
100.00%
99.81%
100.00%
100.0%
100.0%
56.2%
88-95
99.00%
86.37%
91.35%
100.0%
58.0%
39.7%
96+
N/A
N/A
N/A
N/A
N/A
N/A
ALL
99.65%
95.04%
97.59%
95.6%
98.5%
72.3%
Total
ALL
94.3%
98.2%
92.1%
92.7%
94.3%
91.2%
a N/A means that no vehicles failed the applicable IM240 cutpoint within this vehicle class/model
year grouping.
Predictive Retest Algorithms
As with the fast-pass standards, the predictive retest algorithms were developed as part of
SR99-10-02. Retest algorithms are intended to predict whether a vehicle would benefit
from additional testing. If the algorithm determines that a vehicle that was failing at the
end of the first or second of three IM147 tests would benefit from additional testing, then
the next IM147 would begin. If, on the other hand, the algorithm determines that the
vehicle would not benefit from additional testing, the test would be terminated at that
point and the vehicle would fail the inspection.
In the current study, the workplan called for Sierra to refine the algorithms using the
3,347-vehicle sample and the Max CO cutpoints. The original algorithms used a
-39-

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combination of mass and concentration readings during specific modes of the IM147 test
to predict test outcomes.
In conversation with Gordon-Darby staff, however, Sierra learned that the suggested
retest logic would be difficult to implement. While gas concentrations could be
determined during the test, it would be far less work if all of the retest criteria referred
only to mass. Another concern, as previously mentioned, was that the modes did not
align with the fast-pkss segments. With these issues in mind, Gordon-Darby asked if
Sierra could find a more user-friendly retest logic.
While Sierra agreed to explore alternatives to concentration measurement in the retest
procedure, it was not clear that any viable alternatives existed since the justification for
using both concentration and mass made sense technically. While mass emission
measurements are useful to gauge emission performance relative to the actual cutpoint,
the concentration measurements provide a measure of emission performance less affected
by engine load than mass emissions. As a result, emissions concentration measurements
seemed especially relevant for measuring improvements in engine performance during a
transient test.
After some experimentation, Sierra settled on a procedure that utilizes the previously
determined fast-pass regression coefficients to predict whether a vehicle would benefit
from additional testing. In short, if the vehicle has not fast-passed the inspection by the
end of the 19th segment and the predicted emissions fall outside a tolerance level
allowing automatic retest, the emissions result predicted after segment 7 is compared to
the emissions result predicted after segment 19 to determine whether the vehicle
emissions are converging on the applicable cutpoint. Equation (5-2), named the
convergence ratio for this study, illustrates how emission convergence on the applicable
standard is determined.
Where:	Segment7
Segment 19
Comp_Std
X
According to this equation, the convergence ratio would be greater than one if the
emissions are converging on the cutpoint for vehicles where the emissions are above the
cutpoint. A convergence ratio less than 1, on the other hand, suggests that the emissions
are actually diverging from the cutpoint. With this in mind, it makes sense that a
conservative decision threshold for the retest algorithm would utilize a convergence ratio
greater than one for the HC and CO channels. In general, emissions of both of these
Segmentl- Comp_Std ^
Segment\9- Comp_Std	^
= Predicted emissions after segment 7 (Composite
Regression)
= Predicted emissions after segment 19 (Composite
Regression)
= Composite Cutpoint for the specific emission
= Empirically determined criteria
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pollutants reduce as a vehicle warms up. NOx, on the other hand, may actually increase
the longer a vehicle operates, so the appropriate convergence ratio threshold may be
greater than one.
Iterative solutions to this equation using actual vehicle data suggest, however, that both of
these assumptions may result in overly stringent application of the retest algorithm, thus
creating unacceptable false failure rates. In short, some vehicles failing for either HC or
CO during one of the early IM147 tests may go on to pass a later IM147 in spite of the
fact that their emissions appeared to be diverging from the cutpoint during the earlier
IM147. Therefore, the convergence threshold for HC and CO may have to be greater
than one to safely predict convergence. Regarding NOx emissions, modal emissions are
even more difficult to predict. As a result, the convergence ratio was not applied to the
NOx channel. Instead, the retest algorithm predicts NOx failures by simply comparing
segment 7 and 19 NOx readings to the previously mentioned tolerance level. If the
readings are greater than the prescribed multiple of the cutpoint, then the vehicle does not
receive a retest.
Separate algorithms were developed for LDGVs and LDGTs based upon whether one or
two IM147 tests had been completed. In all cases, a predicted emissions score of less
than the standard (but that does not trigger a fast-pass) is treated as cause for a retest.
This approach avoids the need to deal with negative convergence ratios, while also
providing maximum potential for vehicles to pass the test. The flow charts shown in
Figures 5-2 through 5-5 show the specifics.regarding how these algorithms work.
Unlike the fast-pass algorithms, the error term in the fast-pass regression equations is not
included when determining predicted emissions for the Segment 7 to Segment 19
comparison. If the error term (which varies between regression models) were included,
the retest procedure would need to be refined for specific regression models in addition to
vehicle types. Without including the error term, LDGVs can simply be separate from
LDGTs while still maintaining acceptable accuracy.
Table 5-4 presents the results of the retest algorithm independent of the fast-pass
algorithm. As shown in the table, the retest criteria eliminate more vehicles after the
second test than after the first test. This is in contrast to the fast-pass criteria, which pass
a disproportionate number of vehicles after the first IM147 when compared to the second
IM147. Given the difference between the two criteria, this makes sense. While vehicles
can pass the test after early IM147 tests without the fast-pass enabled, the only way a
failing vehicle can end the test without the retest algorithm enabled is to run the full
duration of the test. As a result, the retest criteria on the first IM147 are judged by their
ability to predict emissions on the third IM147 whereas the fast-pass criteria is judged by
its ability to predict emissions on the current IM147, an easier task. In turn, the retest
criteria must be correspondingly more conservative on the first IM147 than the fast-pass
criteria.
In addition to listing the point at which vehicles were denied a retest, Table 5-4 lists the
number of vehicles falsely failed as a result of the retest procedure. The criterion for false
failing is quite simple; a false failure occurs any time a vehicle, which would go on to
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Figure 5-2
Retest Algorithm During First IM147 - LDGV
Max CO Outpoints
Note: STD = Composite Standard for the specific pollutant.
Mode 7 Predicted = Predicted emissions after 7 segments using the composite regression
equations without error term included.
Mode 19 Predicted = Predicted emissions after 19 segments using the composite
regression equations without error term included.
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Figure 5-3
Retest Algorithm During Second IM147 - LDGV
Max CO Cutpoints
Note: STD = Composite Standard for the specific pollutant.
Mode 7 Predicted = Predicted emissions after 7 segments using the composite regression
equations without error term included.
Mode 19 Predicted = Predicted emissions after 19 segments using the composite
regression equations without error term included.
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Figure 5-4
Retest Algorithm During First IM147 - LDGT1, LDGT2
Max CO Cutpoints
Note: STD = Composite Standard for the specific pollutant.
Mode 7 Predicted = Predicted emissions after 7 segments using the composite regression
equations without error term included.
Mode 19 Predicted = Predicted emissions after 19 segments using the composite
regression equations without error term included.
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Figure 5-5
Retest Algorithm During Second IM147 - LDGT1, LDGT2
Max CO Outpoints
Note: STD = Composite Standard for the specific pollutant.
Mode 7 Predicted = Predicted emissions after 7 segments using the composite regression
equations without error term included.
Mode 19 Predicted = Predicted emissions after 19 segments using the composite
regression equations without error term included.
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Table 5-4
Retest Algorithm Results
LDGV LDGT1, LDGT2
Total Number of
Complete Tests
1567
1780
# of Failures Without
Retest Algorithm
327 (20.9% of 1567)
273 (15.3% of 1780)
# of Correctly Identified
Failures2
245 (74.9% of 327)b
180 (65.9% of 273)"
# Failing After 1 IM147
94 (38.4% of 245)c
97 (53.9% of 180)
# Failing After 2 IM 147s
151 (61.6% of 245)
83 (46.1% of 180)c
# of Passing Vehicles
Falsely Failed by Retest
0(0% of 1567)
0(0% of 1780)
""'Correctly identified failures" refers to those vehicles that were still failing at the end of the third
IM147.
bThe number shown in parentheses is the number of failures without the retest algorithm.
The number shown in parentheses is the total number of IM147 Cycle 2 and 3 failures.
pass one of the subsequent IM147 tests, is denied a retest. As shown, there were no false
failures. Despite the conservatism evidenced by this result, average test time dropped
from 125 seconds to 96 seconds when the retest algorithm was added to the fast-pass
criteria.
Modal Fast-Fail Criteria
One of the requirements this work assignment was to develop modal fast-fail criteria.
Unlike the retest procedure, which can terminate the test at the ends of the individual
IM147 tests, the fast-fail algorithm can terminate tests during an IM147 test.
As with the retest algorithms, the fast-fail algorithm has the potential to falsely fail
vehicles that would otherwise pass the inspection in the algorithm's absence. After
looking at the test data, it quickly became apparent that the data from the first IM147
were too unpredictable to fast-fail any significant number of vehicles during the first
IM147 without also significantly increasing false failure levels. While the retest
algorithm utilizes almost all of the first IM147 data before it makes a decision not to
retest a vehicle, the fast-fail algorithm must make essentially the same decision in a
smaller amount of time. For this reason, it was decided that the fast-fail algorithm would
not function before the second IM147.
In order to build on work already completed for the retest procedure, the fast-fail
algorithm for the second IM147 trace evaluates predicted emissions after segment 7 of
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the drive trace. To maintain uniformity for lane software programmers, the error term
was not included in the prediction or the mode 7 emissions since it was not in the retest
algorithm.
The vehicle fast-fails the second IM147 if any of the following are true:
For LDVs:
Predicted HC after 7 segments > (1.5 x Composite HC standard)
Predicted CO after 7 segments > (2.2 x Composite CO standard)
Predicted NOx after 7 segments > (1.4 x Composite NOx standard).
ForLDTs:
Predicted HC after 7 segments > (1.1 x Composite HC standard)
Predicted CO after 7 segments > (1.5 x Composite CO standard)
Predicted NOx after 7 segments > (1.5 x Composite NOx standard)
Using this criteria, 99 trucks and 124 cars are fast-failed after segment 7 of the second
IM147, with no false failures when the retest algorithm is disabled.
During the third IM147, the potential for falsely failing vehicles in subsequent IM147
tests is eliminated. As a result, the modal fast-pass algorithm, with a minor modification,
can be implemented to predict failing vehicles. Instead of adding the error term to the
predicted score as is done with the fast-pass algorithm, the error term is subtracted. This
adjustment ensures conservative emission estimates, which will help to minimize false
failures when using the algorithm. Equation 5-3 illustrates the third IM147 fast-fail
algorithm.
Where: P240
Cn
Snm
Xnm
Me
*It is theoretically possible to identify additional fast-fails by using the same or different predictive
algorithms at the end of subsequent segments. While Gordon-Darby has expressed interest in this
enhancement in order to further reduce average test time, the timing of the study did not allow for the
evaluation of this issue.
P240  Cn + J] {iSnm X JCnm}  ( Me X En)
m= 1
Predicted emissions after completing n segments
. Regression intercept for equation n
Regression coefficient for segment m in equation n
Total emissions over a given segment m in equation n
Error multiplier (usually 2 unless otherwise specified)
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En = Error in regression equation n
n = Equation number (corresponds to the number of modal
segments completed)
m = Segment number
Table 5-5 details the results of the fast-fail algorithm for both the second and third IM147
tests. As the number of false failures indicates, the fast-fail criteria was developed with
the intention of minimizing false failures. The results shown in the table are also
deceptive, since they are based on an analysis of the impact of the fast-fail algorithm in
the absence of the other test criteria. While over 600 fast-failures are shown in the table,
many of these are also subject to the retest criteria, resulting in a much smaller fast-fail
impact when the criteria are combined. In this latter case, the number of fast-fails falls to
roughly 200 vehicles. This effect, combined with the fact that such fails occur relatively
late in the 3-IM147 test cycle, leads to a fairly small impact on average test me. Once the
fast-fail algorithm was enabled with the fast-pass and retest algorithm, average test time
was reduced from 96 seconds to 94 seconds.

Table 5-5
Fast-Fail Algorithm

Vehicle Class
Second IM147
Fast-Failures
Third IM147
Fast-Failures
False
Failures
LDV
124
214
3
LDT
99
182
3
Total
223
393
6
Integration of Fast-Pass. Retest. and Fast-Fail Algorithms
The next step in the analysis was to integrate the Fast-Pass, Retest, and Fast-Fail criteria
to determine their net effect. Table 5-6 shows how average test times and excess
emissions identified vary by model year range and vehicle class using the integrated
criteria.
As you can see, once the retest and fast-fail algorithms were added to the fast-pass
algorithm, excess emissions identification improved. There are two reasons for this: (1)
vehicles that would be falsely passed later in the test are now failed prior to that decision
being made; and (2) some vehicles that passed according to the IM147 criteria yet would
have failed the IM140 are now falsely failed on the IM147, thus increasing IM240 excess
emissions identification.
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Table 5-6
Comparison of Integrated Algorithms (No CPP) vs. Standard IM147
Impact on Test Time and Excess Emissions Lost
Class
Model
Year
Group
Sample
Size'
Mean Test
Time
Standard*
Mean Test
Time w/
Algorithms*
% Excess Emissions Identified"
HC
CO
NOx
LDGV
1981-82
105
286.4
140.1
-
87.6%
100.0%
1983-85
228
311.2
169.4
100.0%
97.4%
100.0%
1986-89
 425
248.4
112.0
99.3%
97.5%
100.0%'
1990-95
952
184.8
78.3
96.9%
85.3%
91.4%
1996+
70
154.3
37.1
-
-
-
All
1780
221.0
100.0
98.6%
93.9%
97.6%
LDGT1
1981-85
260
306.6
158.7
75.7%
97.5%
98.4%
1986-89
222
230.8
101.6
92.4%
91.0%
99.6%
1990-95
450
173.3
59.2
0.0%
0.0%
100.0%
1996+
32
155.1
31.6
-
-
100.0%
All
964
221.9
94.9
79.3%
94.4%
98.7%
LDGT2
1981-85
94
307.5
158.8
100.0%
100.0%
100.0%
1986-87
64
253.2
101.4
100.0%
100.0%
70.8%
1988-95
427
166.5
54.2
100.0%
58.0%
39.7%
1996+
18
146.0
28.0
-
-
-
All
603
197.1
74.7
100.0%
98.5%
86.0%
Weighted Average
3347
217.0
94.0
94.4%
95.4%
95.7%
The excess emissions identification shown in the above table is down from the
identification of SR99-10-021, which identified 99.6% of the HC emissions, 98.2% of the
CO, and 99.9% of the NOx with the fast-pass and retest algorithms enabled. However,
direct comparison of these results may not be relevant for several reasons. First, the
previous study measured excess emissions captured against the Final Outpoints rather
than the Max CO cutpoints developed for this study. Second, because fast-fail was
created for this study, it was not included in the previous study results. Third, since the
retest algorithm has been modified as part of this study, it will have a different effect on
the results. In the previous study, the retest algorithm improved excess emission
identification by 2.7% versus 1.1% for this study. Lastly, most of the vehicle data used in
this study were collected with the model year exemptions in place. This skews the model
Mean test time standard refers to the average dynamometer test time without the algorithms enabled.
This was determined using the 3347-vehicle sample.
"Percent of IM240 (Max CO) excess emissions identified with the integrated algorithms enabled. This
was determined using 835-vehicle sample.
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year distribution older when compared to SR99-10-02, which used data unbiased by
model year exemptions.
Figure 5-6 illustrates how the integrated criteria combined to produce emission results
and final test times. Note that only 5 of the 3347 vehicles were tested the full duration of
all three IM147 tests.
Figure 5-5
Integrated Test Results
Maximum CO Outpoints Without CPP Limits
TOTAL PASS = 2,802	TOTAL FAIL = 545
TOTAL FALSE FAILURES = 3
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Segment 2 Revised Integrated Algorithms Results
The original integrated algorithm results were determined assuming fast-pass and fast fail
results could not be rendered prior to the fourth segment (i.e., no earlier than Test Time =
28 seconds). This was consistent with the procedure established in SR98-02-01.
Gordon-Darby, wishing to further minimize test time, requested that Sierra explore the
feasibility of rendering fast-pass and third IM147 fast-fail decisions after earlier segments
without degrading excess emission identification. Unfortunately, simply moving the
decision forward with the existing algorithms, while shortening test time, did degrade
excess emission identification. To adjust for this change, the error multiplier used in the
fast-pass decision needed to be increased to 3 during segment 2 and to 2.5 during segment
3. While the fast-fail algorithm used during the third IM147 also uses an error multiplier,
it can be left at 2 during segments 2 and 3 without increasing false-failure incidence.
Using these criteria, the earliest possible fast-passes were moved to the end of segment 2
(i.e., Test Time =16 seconds) and average test time was reduced to 90.5 seconds without
sacrificing any excess emissions identification. Excess emissions identification remained
at 94.4% for HC, 95.4% for CO, and 95.7% for NOx.
SIP Credit Analysis
The comparison of excess emissions identification between the IM240 and IM147 that is
presented above is based on the use of CO Max standards for both test cycles. However,
to develop an estimate of the allowable SIP credit that should be allocated to the revised
IM147 CO Max standards, it is also necessary to compare excess emissions identification
between this scenario and the IM240 with EPA-recommended final cutpoints in place.
This is due to the need to establish a link to using MOBILE for SIP modeling purposes.
Configuring MOBILE with CO Max standards is not feasible; therefore, a better
approach is to run the model with final EPA standards in place and use the excess
emissions identification rates developed in this study to adjust the resulting outputs.
Table 5-7 shows the excess emission identification rates when the IM147 Max CO
cutpoints are compared to the IM240 final standards. Pollutant-specific identification
rates are shown both without and with the fast-pass, retest, or fail-fail algorithms enabled.
(The latter scenario includes fast-passing vehicles as early as at the end of segment 2.)
Since Arizona will be implementing the IM147 test procedure with the algorithms
enabled, the identification rates for this scenario are the ones that should be used to adjust
the MOBILE modeling results (based on final IM240 standards) for SIP credit purposes.
As expected, the table shows that HC and NOx identification rates are significantly lower
with the IM147 Max CO cutpoints relative to final IM240 standards. This is due to the
fact that the Max CO cutpoints are designed to maximize the CO benefits of the program
at the expense of HC and NOx benefits, while keeping maximum failure rates in each
cutpoint category to acceptable levels. The CO identification rate of 97.9% (with the
algorithms enabled) shows that the Arizona program will achieve nearly all of the
modeled benefit of the final IM240 standards. Note that this will be substantially more
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Table 5-7
Comparison of IM147 Max CO Cutpoints to IM240 Final Standards
Impact on Excess Emissions Lost*
Class
% Excess Emission Identified
(Without Fast-Pass, Retest, Fast-Fail)
% Excess Emissions Identified
(With Integrated Algorithms)
HC
CO
NOx
HC
CO
NOx
LDGV
94.1%
96.5%
85.6%
91.7%
95.6%
86.2%
LDGT1
75.6%
100.0%
79.5%
65.0%
98.1%
81.2%
LDGT2
89.0%
100.0%
53.4%
98.5%
100.0%
59.6%
Weighted
Average
86.5%
98.8%
75.1%
85.6%
97.9%
77.5%
* Percent of IM240 (Final Standards) excess emissions identified was determined using the 835 vehicle
sample.
effective than the current phase-in IM240 standards. The table also shows that the
addition of the integrated algorithms results in less than a 1 % reduction in the excess
emissions identification rate for CO.
Need for Follow-Up Analysis
As discussed above, the analysis results presented in the report are based on a relatively
small sample of IM147 and IM240 data. While the available data are significantly more
robust than the previous sample of 300 vehicles, it is clear that these results should be
revisited with a much larger sample once IM147 testing is initiated in Arizona. We
therefore recommend that as soon as one to two months of IM147 data are collected in
the program, they should be used to verify the validity of the cutpoints and algorithms
developed in this study. This follow-up analysis would allow for any required fine-
tuning of the cutpoints and algorithms.
###
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6. DEVELOPMENT OF IM147 VARIATION LIMITS
In addition to fast-pass/fail outpoints and retest criteria, trace variation limits were also
developed for the IM147 test. Under Task 6 of the Work Assignment, an analysis
methodology that was used to develop IM240 variation limits under an earlier EPA
study2 was applied to the pilot IM147 data to develop similar limits for the IM147 test.
During the course of the effort, an alternate statistical metric, Cumulative Positive
Specific Power (CPP), was identified that resulted in better second-by-second variation
limits than the Positive Kinetic Energy (PKE) metric employed in the previous EPA
study. This section of the report describes why the new statistic, was selected, and how
IM147 CPP variation limits were developed and evaluated to ensure they do not produce
excessive test abort rates. It also assesses their individual impact on average
dynamometer test time. (The combined effect of fast-pass/fail cutpoints, retest criteria
and IM147 trace variation limits is discussed in Section 7.)
Before describing the effort performed under the current Work Assignment, a review of
the existing IM240 tolerance limits and a summary of the previous evaluation of those
limits are presented.
Existing Tolerance Limits
The prescribed driving cycles for the transient IM240 and IM147 tests consist of varying
second-by-second speeds ranging from zero (i.e., idle) to 56.7 mph, with maximum speed
changes of 3.3 mph/sec. During actual I/M testing, the driver watches a graphical
display of the prescribed or "reference" speed/time trace overlayed with the actual
second-by-second trace as it is being driven as an aid to following the reference trace and
anticipating upcoming speed changes. (The visual display also indicates prescribed shift
points for manual transmission vehicles along the trace.)
Since each vehicle has different performance characteristics, it is impossible, even for
highly skilled drivers, to precisely follow the second-by-second reference trace speeds
during actual "one-time-only" testing. As a result, EPA originally developed a set of
speed-based tolerance limits for the IM240 test that defined the leeway allowed to the
driver in trying to follow the reference trace for the test to be considered valid. Those
tolerance limits consisted of two components: (1) speed excursion limits; and (2) speed
variation limits. Each of these criteria is described below.6
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Speed Excursion Limits f85.2221 (e) (4)1 - Speed excursion limits shall apply as
follows:
(i).	The upper limit is 2 mph higher than the highest point on the trace within
1 second of the given time.
(ii).	The lower limit is 2 mph lower than the lowest point on the trace within 1
second of the given time.
(Hi). Speed variations greater than the tolerances (e.g., during gear changes)
are acceptable provided they occur for no more than 2 seconds on any
occasion.
(iv). Speeds lower than those prescribed during accelerations are acceptable
provided they occur for no more than 2 seconds on any occasion.
Speed Variation Limits [85.2221 (e) (5)1
(i). A linear regression of feedback value on reference value shall be
performed on each transient driving cycle for each speed using the method
of least squares, with the best fit equation having the form: y = mx + b,
where:

(A).
y = the feedback (actual) value of speed;
(B).
m = the slope of the regression line;
(C).
x = the reference value; and
(D).
b = the y-intercept of the regression line.
(ii). The standard error of estimate (SE) ofy on x shall be calculated for each
regression line. A transient driving cycle lasting the full 240 seconds that
exceeds the following criteria shall be void and the test shall be repeated:
(A).	SE = 2.0 mph maximum.
(B).	m= 0.96 -1.01.
(C).	r2 = 0.97 minimum.
(D).	b   2.0 mph.
Simply stated, the speed excursion limits require that vehicles be driven within 2 mph of
the reference trace, accommodating for gear changes and other momentary excursions.
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The speed variation limits, though a bit more difficult to comprehend, were intended to
ensure that speed differences from the reference trace within the 2 mph excursion limits
"envelope" would not bias the resulting measured emissions.
However, EPA suspended the use of the speed variation limits on November 23, 1993,
pending further evaluation.
Previous Analysis of Speed Variation Limits
Inadequacy of Speed Variation Limits - In 1998, Sierra completed a study for EPA2 that
evaluated the ability of the linear regression-based speed variation limits to identify high
emissions-producing speed variations. It was found that the linear regression criteria
were inadequate in flagging high-emissions speed variations. The reason for this finding
was that the standard error (SE) statistic does not give "appropriate" higher weighting to
speed deviations occurring at the critical high-emission points along the IM240 trace.
Instead, it gives equal weighting to all speed variations and thus (along with the other
regression statistics used in the speed variation criteria) is ill-suited to identifying those
speed deviations that substantially affect IM240 emissions.
Evaluation of Alternative Statistics - During this study, two alternative statistical
measures were evaluated for their ability to better identify IM240 speed variations that
significantly affect measured emissions:
1.	DPWRSUM7 - the sum of absolute changes in specific power; and
2.	Positive Kinetic Energy fPKE) - the sum of positive differences in kinetic
energy per unit distance.
It was found that the PKE statistic provided a better measure than DPWRSUM for
identifying those speed variations from the reference trace that produce high emissions.
This finding was supported by analysis of modal (i.e., second-by-second) speed and
emissions data from a random sample of 16,581 full* IM240 tests from the Arizona I/M
program. It was determined that the high-emission portions of the IM240 test closely
corresponded with periods of acceleration. A examination of both the DPWRSUM and
PKE statistics found that DPWRSUM is increased during both decelerations and
accelerations. PKE, on the other hand, is increased only during acceleration periods.
Thus, the DPWRSUM statistic is "diluted" with speed variations during decelerations
that have little effect on emissions. As a result, the PKE statistic was reasoned to provide
a better measure of significant emissions-producing speed variations.
Development of PKE-Based IM240 Variation Limits - From this finding, PKE variation
limits were then developed as potential replacements to the original regression-based
*
"Full" tests refer to those run over the entire test duration, regardless of fast-pass or fast-fail status.
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speed variation limits. The basic approach used to develop PKE-based speed variation
criteria for the IM240 consisted of the following elements:
1.	Establishing upper and lower "composite" PKE limits for full 240-second
tests from the Arizona data sample; and
2.	Scaling these composite limits based on the cumulative PKE at each second of
the IM240 reference trace to produce second-bv-second PKE variation limits.
Second-by-second PKE variation limits were established to ensure that compliance with
the reference trace was maintained throughout the test to minimize emissions bias during
fast-pass and fast-fail determinations.
From analysis of the Arizona data, the PKE variation limits were established over a range
expected to produce no more than a 3% increase in the test abort rate when applied as a
replacement for the speed variation criteria, in conjunction with the existing speed
excursion criteria.
Development of Positive Power-Based Variation Limits
Under the current study, the randomly collected triplicate Arizona IM147 tests described
in Section 3 were also analyzed to develop IM147 trace variation limits. Similar to the
earlier IM240 study, upper and lower composite limits were first established over the full
duration of the IM147 test. The composite variation limits were then scaled at each
second of the reference trace to produce second-by-second variation limits.
Before discussing the details of how these IM240-developed methodologies were adapted
for the IM147 test, an explanation of why positive power was used as a replacement for
PKE as the variation limits metric is provided.
Use of Positive Power Instead of PKE - During the course of developing the second-by-
second IM147 variation limits, a number of tests were identified for which PKE-based
variation limits were being exceeded under periods of deceleration. This was clearly a
problem. As discussed earlier, statistical metrics used to establish variation criteria were
selected based on their ability to identify high emissions-producing variations that
coincide with acceleration events. Although the composite PKE statistic does this well
on a cumulative basis over the entire test, it is less-suited when applied on a second-by-
second basis.
The reason for this can be seen by first considering how PKE is calculated. Over a
traveled driving cycle of distance x, cumulative PKE per unit distance is defined as
follows:
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I PP'
CPKE(x)=x		[6-1]
J dx
The term PP, is referred to as the positive specific power at time t and is given by the
following equation:
fV,2-(V,-,)2 whenV
PP' f 0 whenV, 
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Figure 6-1
Comparison of 1M147 Variation Limit Metrics
Time (seconds)
example, this phenomenon can be seen at Seconds 11-14 in Figure 6-1, where the
reference trace exhibits acceleration from Second 5 through Second 14 but cumulative
PKE begins dropping beyond Second 11.
Because of this behavior, it was difficult to establish reasonable second-by-second
variation limits based upon cumulative PKE differences between the reference and driven
traces using a scaling approach similar to that developed under the 1998 IM240 study.
That approach basically consisted of scaling the composite PKE interval limits
established at the end of the test by the percentage difference of these limits from the
composite reference value.
Since the magnitude of critical emissions-affecting deviations during accelerations is
difficult to distinguish from the magnitude of deviations that do not substantially affect
emissions during deceleration and cruise, this scaling approach fails when based upon
cumulative PKE. The result is that cumulative PKE-based second-by-second variation
limits of a specific scaled interval width will either falsely flag less important deviations
during decelerations or not identify deviations during accelerations that do affect
measured emissions.
Thus, two alternate approaches were considered for establishing reasonable second-by-
second IM147 variation limits:
1.	Use of a better-behaved statistic, such as positive power, in conjunction with
the basic scaling approach; and
2.	Development of separate variation limits for each second using deviation
distributions (reference vs. actual) at each second compiled from a full
modal analysis of the test data sample.
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Although conceptually appealing since variation limits are established independently for
each individual second, the latter approach would require a rigorous modal analysis just
to develop initial variation limits for each second. A complex iterative process would
then be required to evaluate these limits over the entire trace and "tune" them in a manner
that yields an acceptable overall abort rate. This tuning step would also consider the
relative impact of trace variations at each second on measured emissions. For example, it
would be desirable to apply tighter trace variation limits during acceleration segments
that produce high emissions than during less emissions-significant idling segments if the
resulting overall abort rates (across all segments) can be kept at acceptable levels.
In addition, it is believed that imposing tighter, independently established second-by-
second trace variation limits would result in a much greater degree of "re-learning" by the
lane inspectors as they adjust to the impact of these limits. This re-learning process and
any necessary re-tuning of the variation limits could best be evaluated through an initial
pilot study before being implemented on a program-wide basis. During this pilot study, it
is also envisioned that different approaches to providing visual feedback to drivers as
they attempt to follow the trace could be evaluated. For example, this could involve
providing dynamically updated forward-looking "trace envelopes" or speeds that guide
the driver back toward the reference trace when a nominal excursion begins in a manner
that complies with the scond-by-second variation limits.
As a result of the scope of the latter approach, the first approach was selected because it
was believed to substantially overcome the shortcoming of the PKE-based metric while
being less resource-intensive to apply and test than limits developed from a full modal
analysis. It should also be noted that using CPP as a replacement for PKE assumes that
the existing speed excursion criteria developed by EPA will be applied in conjunction
with CPP-based variation limits. This assumption is dictated by the use of a cumulative
metric in specifying second-by-second variation limits.
Development of Composite Variation Limits - Composite IM147 variation limits were
generated using a similar methodology to that employed in the 1998 IM240 study, except
cumulative positive power (CPP), rather than cumulative PKE, was used as the statistical
metric.
Figure 6-2 shows the distribution of composite (i.e., 147-second) CPP calculated from the
second-by-second actual speeds in the Arizona data sample, expressed as the percent
difference between actual and reference IM147 CPP.
As the figure shows, actual CPP appears normally distributed, although the median CPP
is approximately 1% higher than the reference value. To determine how far to go along
the "tails" of the CPP differences distribution to set composite limits, CPP differences
among the test lane drivers in the Arizona data were examined. The basic concept
applied in setting the CPP limits was to identify a significant fraction of drivers who,
historically, could always (or nearly always) run IM147 tests within the selected CPP
limits. Given a mixture of ability among individual drivers to follow the reference trace,
Sierra sought to identify the fraction of "competent" drivers who could follow the trace
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Figure 6-2
Distribution of Arizona IM147 Positive Power Differences
% Difference Between Reference and Actual Positive Power
(Sample Size = 9,306)
14%
-10% -9% -8% -7% -6% -5% -4% -3% -2% -1% 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15%
Percent Difference from Reference Value
more consistently than others when conducting IM147 tests for a range of vehicles. This
subset of competent drivers and tests was used to establish composite CPP limits.
From the Arizona data sample of over 10,000 valid triplicate IM147 tests, records in
which speed excursions (over 2 mph from the reference trace) occurred were discarded,
leaving a remaining sample of 9,306 tests. For the purpose of establishing composite
CPP variation limits based on the capabilities of "good" drivers, speed excursion tests
were removed from this portion of the analysis.
The remaining data sample was then sorted into groups by individual driver. A total of
231 drivers were found in the sample. Driver groups containing fewer than 25 tests were
then discarded; this left a total of 112 driver groups, which encompassed 86% of the tests
in the total sample (i.e., before discarding small-sample driver groups). Composite CPP
was then calculated for each test in the remaining driver groups. The mean and standard
deviation of CPP from the tests within each driver group were also computed. The driver
groups were then ranked by increasing CPP standard deviation and the top 50% of the
drivers (based on lowest CPP standard deviation) were used to compute possible
composite CPP cutpoints.
Table 6-1 lists a series of possible CPP cutpoints computed from the percentile CPP
variance among the top 50% drivers. For example, the CPP cutpoints shown for the 2%
row under the "Top 50% Percentile" column (the third column in Table 6-1) indicates
that 96% (100% - 2 x 2%) of the tests from the top 50% drivers had composite CPP
within 4,437 and 4,949 mph2/sec.
* Identified by the four-digit "InspectorlD" field in the database.
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Table 6-1
Preliminary CPP Cutpoints (mph2/sec) Based on Top-50% Drivers
(Sample Size = 7,962 Tests)
Top-50%
Driver
Lower Tail
Top-50%
Driver
Upper Tail
Top-50%
Driver
Percentile
Low-End
CPP
(mphVsec)
High-End
CPP
(mph2/sec)
Interval
Width
(mph2/sec)
0.25%
99.75%
0.25%
4,348
5,077
728
0.5%
99.5%
0.5%
4,368
5,039
671
0.75%
99.25%
0.75%
4,391
5,013
622
1.0%
99.0%
1.0%
4,403
4,987
584
2.0%
98.0%
2.0%
4,437
4,949
512
3.0%
97.0%
3.0%
4,459
4,920
462
4.0%
96.0%
4.0%
4,475
4,903
428
5.0%
95.0%
5.0%
4,483
4,882
398
Note that these preliminary outpoints are not centered about the IM147 reference CPP
value of 4,617 mph2/sec (as evidenced by the shifted CPP distribution shown earlier in
Figure 6-2). To generate a series of "final" composite limits for evaluation, Sierra
applied the interval widths shown in Table 6-1 to the reference CPP value to produce
"centered" limits about the reference value.
Incremental abort test rates for each set of centered cutpoints were then calculated based
on both the entire 9,306 test Arizona data sample and the top-50% driver subset. The
results are presented in Table 6-2, which lists both "simple" and "effective" abort rates.
Simple abort rates represent the fraction of tests in the sample for which the composite
CPP cutpoints would have been exceeded. Effective abort rates were calculated from
simple rates by subtracting the fractions of emission-pass tests that exceeded the upper
CPP cutpoint and emission-fail tests that exceeded the lower CPP cutpoint. The idea is
that tests on vehicles that had passing emission scores but were driven with high CPP
should not be aborted. Similarly, tests that failed on emissions despite being driven
below the lower CPP cutpoint should also be considered valid tests and not aborted.
Thus, tests should be aborted only when the upper CPP variation limit is exceeded for an
emissions failure or the lower CPP variation limit is exceeded during a passing test. The
emissions pass/fail determinations used to calculate the effective abort rates shown in
Table 6-2 are based upon the "Max CO" cutpoints developed earlier in the study.
In addition to the calculated test abort rates, Table 6-2 also shows the percentage of
drivers who are always within the limits of each set of CPP cutpoints.
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Table 6-2
Centered CPP Cutpoints and Resulting Test Abort Rates
Top-50%
Driver
Percentile
Centered CPP Limits
(mphVsec)
Abort Rates (%)
from All Tests
Abort Rates (%) from
Top-50% Driver Tests
Percentage of All
Drivers Within Limits
Lower
CPP
Limit
Upper
CPP
Limit
Interval
Half-
Width
Simple
Abort
Rate
Effective
Abort
Rate
Simple
Abort
Rate
Effective
Abort
Rate
> Lower
Limit
< Upper
Limit
0.25%
4,253
4,982
364.2
5.8%
1.6%
1.2%
0.2%
95.7%
53.9%
' dbO.5%
4,282
4,953
335.6 T
; 7.2%
r 2.1%
1.9%
.0.4%
93.5%.
'47.4%
0.75%
4,307
4,928
310.8
8.7%
2.6%
2.7%
0.6%
91.8%
40.5%
1.0%
4,325
4,909
292.1
10.5%
3.1%
3.7%
0.9%
88.4%
37.1%
2.0%
4,361
4,873
255.9
14.1%
4.2%
6.3%
1.6%
81.5%
28.9%
3.0%
4,386
4,848
230.9
17.3%
5.3%
8.9%
2.6%
77.6%
26.3%
4.0%
4,403
4,831
214.2
19.9%
6.2%
10.9%
3.1%
74.1%
24.6%
5.0%
4,418
4,816
199.1
22.6%
7.2%
13.2%
3.9%
67.2%
22.4%
Sample
Size
-
-
-
9,306 Tests
7,962 Tests
231 Drivers
Based on the results given in Table 6-2, Sierra proposes the use of composite lower and
upper CPP variation limits of 4,282 and 4,953 mph2/sec, respectively (shown in the
shaded row in Table 6-2). As indicated in the table, these composite CPP limits are
expected to increase the (effective) abort test rate by 2.1% relative to that resulting from
EPA's recommended speed excursion limits based on available test data. Since the goal
of the analysis was to specify variation limits that kept the abort rate due to these
variation limit violations to 3%, composite CPP limits resulting in only a 2.1%
incremental abort rate were selected. This left some "room" below the 3% target for the
impact of also imposing second-bv-second CPP variation limits.
If drivers are selected based on their ability to perform as well as the best 50% of the
current drivers, then the abort rate would drop to just 0.4%. In practice, it is expected that
the abort rate will increase by less than this amount as drivers "adjust" to the new limits.
Development of Second-bv-Second Variation Limits - Using the recommended
composite CPP limits of 4,282 and 4,953 mph2/sec, second-by second CPP limits were
generated by scaling the percentage difference of these limits from the composite
reference value (7.3%) to the CPP calculated at each second from the reference trace.
This approach was further modified as described below.
1. To provide drivers with a short period to "learn" to drive each test vehicle,
second-by-second CPP limits were not imposed until t=30 seconds; and
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2. To further accommodate wider allowable variations (on a percentage basis)
in second-by-second CPP at the beginning of the transient IM147 test, a
"CPP Multiplier" factor was applied that widened the allowed CPP limits
progressively from the end of the test to where limits begin at t=30 seconds.
From its maximum value beginning at t=30 seconds, the CPP multiplier
factor was linearly decreased to a value of unity (i.e." 1.0) at t=146. In other
words, at the end of the test, the CPP limits were set equal to the composite
CPP limits. Furthermore, this linear narrowing was applied only over the
acceleration sections of the IM147 trace, during which the reference CPP is
increasing. During cruise and deceleration periods, the limit widths were
held constant (as the reference CPP also remains constant).
This latter improvement to the methodology employed in the 1998 IM240 study, in
conjunction with the use of CPP instead of CPKE, enabled second-by-second IM147 CPP
limits to be specified so variation limit aborts were not falsely triggered during
deceleration and cruise portions of the transient test. Note that second-by-second CPP
variation limits developed in this manner can still be exceeded during cruise and
deceleration events, signaling tests that should be aborted. However, falsely triggered
"anomalous" exceedances that occur from the use of a PKE-based metric are eliminated
under this modified approach.
Figure 6-3 illustrates this modified second-by-second CPP-based variation limit concept.
The thick solid line shows the reference CPP over the IM147 test; the thinner solid lines
represent the lower and upper CPP limits established as described above.
These CPP traces are plotted against the left axis. Speed, indicated by the dashed line, is
plotted against the right axis. Note that the CPP limits can be held constant during cruise
and deceleration periods. Thus, variation limit exceedances in these intervals are real,
rather than anomalous artifacts of the statistical metric.
Figure 6-3
. Illustration of Second-by-Second IM147 Cumulative Positive Power Variation Limits
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To establish second-by-second CPP variation limits that produced expected test aborts
near the 3% target rate, a range of initial CPP multipliers (from 2.0 to 6.0) were
evaluated. These initial multipliers specify the width of the variation limits at the starting
point (t=30 seconds) relative to the composite interval width at the end of the test. For
example, an initial CPP multiplier of 2.0 means that the starting interval width was 14.6%
(2.0 x 7.3% composite CPP interval width) of the reference CPP trace at that point.
Figure 6-4 shows the increase in effective abort rate as a function of varying initial CPP
multipliers. The diagonally striped region represents lower CPP variation limit aborts,
the shaded region above shows upper limit aborts. Since these abort events are mutually
exclusive, their sum represents the total expected effective abort rate from
implementation of the CPP variation limits.
Figure 6-4
Effect of Initial CPP Limits Multiplier on Effective IM147 Abort Rate
(Sample Size = 9,306)
Initial CPP Limits Multiplier
Based on the analysis results, an initial CPP multiplier of 3.5 is recommended for
implementation in Arizona. Second-by-second CPP variation limits based on the use of
this multiplier are shown in Appendix E.
Evaluation of IM147 Variation Limits on Test Time
Using the second-by-second CPP variation limits described in the preceding section, an
analysis was conducted of the impact of these variation limits on average dynamometer
test time. This was a simplistic analysis since it addressed only the singular impact of the
variation limits. (A more exhaustive analysis of the combined test time impact of CPP
variation limits in conjunction with fast-pass, fast-fail and retest decisions is presented in
Section 7.)
In this simple analysis, the "without limits" or base average test time was assumed to be
146 seconds, the length of a full IM147 test. This assumption was necessitated by the
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Arizona data sample, which contained only full tests. The recommended CPP limits were
found to produce a total effective abort rate of 3.4% based on the Arizona IM147 data.
The average time at which the aborts occurred under these limits was determined to be
101.6 seconds.
Thus, the "with limits" average test time was then calculated as follows:
With Limits Test Time = Base Test Time + (Abort Rate x Average Abort Time)
= 146 + (3.4% x 101.6 seconds) = 149.5 seconds
It should be noted that this approach also assumes that an aborted test is performed
successfully on the subsequent re-test. Under these assumed conditions, the CPP
variation limits will increase average test times by approximately 2% [(149.5-146)
146].

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7. INTEGRATION OF CPP VARIATION LIMITS
The final phase of this analysis involved integrating the CPP criteria with the other
algorithms included in this study to determine net test time.
As shown in Section 6, there are both high-end CPP errors and low-end CPP errors.
High-end CPP errors occur when the vehicle is driven too aggressively, whereas low-end
CPP errors occur when the vehicle is driven too smoothly, essentially minimizing the
peaks and valleys of the trace. Because high-end CPP errors will create additional
emissions and in turn make it more difficult for a vehicle to pass the test, a test where a
vehicle passed in spite of high-end CPP errors is considered a valid test. If the same
vehicle failed because of high emissions, the cause is assumed to be the high-end CPP
error; therefore, the test would need to be extended to ensure fairness. Low-end CPP
errors, however, would result in lower mass emissions and make it easier for vehicles to
pass falsely. In those cases where the vehicle passes with a low-end CPP error, the result
would be invalid and the test would need to be extended. If a vehicle fails with a low-end
CPP error, the result is valid and the test would terminate. Table 7-1 details which CPP
violations affect which decisions.
Table 7-1
End Test Decisions Affected by CPP Errors
Decision Type Prohibited by:
Fast-Pass
Low-end CPP error
Retest
(initiate another IM147 cycle)
High-end CPP error
Fast-Fail
High-end CPP error
End-of-Test Pass
Low-end CPP error
End-of-Test Fail
High-end CPP error
While the above decision types can be prohibited by the corresponding CPP errors, the
error must occur while data for that decision were being produced in order to affect the
decision. In other words, not all high-end CPP errors occurring during an IM147 test
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would necessarily invalidate failing results, nor would all low-end CPP errors invalidate
passing results. If an applicable power error occurred while data required to make a
particular decision were being collected, then the decision would be invalidated and the
test would continue. Specifically, this would apply to fast-pass and fast-fail decisions.
For example, if the vehicle's emission data were clean enough to permit a fast-pass at
second 40, but there was a low power violation at second 34, then the fast-pass decision
would be invalidated and the test would continue. On the other hand, if the first power
violation occurred after second 40 (e.g., at second 41), then the vehicle could be fast-
passed at second 40.
In a related issue, the CPP error is reset at the conclusion of each IM147. As a result,
low-end CPP errors occurring during the first IM147 do not prohibit pass-oriented
decisions in subsequent IM147 tests. The same is true for high-end CPP errors and fail-
oriented decisions.
Using this logical framework, the CPP variation limits were applied to the 3347-vehicle
sample to determine how this algorithm would affect test time. Since the CPP variation
limits are designed to be imposed in concert with the speed violation limits detailed in the
IM240 guidance, vehicles already failing the speed violation limits were eliminated from
the sample since they would be aborted regardless of the CPP outcome. Of the original
3347 vehicles, 3006 remained after eliminating vehicles with speed violations from the
sample.
The flow chart detailed in Figure 7-1 shows how the CPP decision integrates with the
fast-pass/fail and retest algorithms previously discussed in this report. Note that in cases
where a CPP violation prevented a decision during the third IM147, the vehicle would,
after completing the third IM147, restart the third IM147 again. For the average test time
computation, it was assumed that CPP errors during the third IM147 extended the test
time 146 seconds.
Given the 3006 vehicle sample, the average test time, with the fast-pass, retest, and fast-
fail criteria enabled but without the CPP criteria applied, was 89.07 seconds. Once the
CPP criteria were enabled, 87 vehicles' tests (of the 3006 vehicles) were extended,
increasing the average test time by 0.98 seconds (or 1.1%) to 90.05 seconds. This is less
than the 2% increase projected at the end of Section 6, which makes sense given that the
2% projection was made without the fast-pass/fail and retest algorithms in place. The
overall test time reductions caused by the fast-pass/fail and retest algorithms would mean
that fewer errors would be committed.
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Figure 7-1
###
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8. REFERENCES
1.	"Determination of Emissions Credit and Average Test Times for IM147 Testing,"
Prepared by Sierra Research for the U.S. Environmental Protection Agency, Report
No. SR99-10-02, October 10, 1999.
2.	"Additional Study of Preconditioning Effects and Other IM240 Testing Issues,"
Prepared by Sierra Research for the U.S. environmental Protection Agency, Report
No. SR98-02-01, February 2, 1998.
3.	Heirigs, P.L. and J. Gordon, "Preconditioning Effects on I/M Test Results Using
IM240 and ASM Procedures," SAE Paper No. 962091, 1996.
4.	Whitby", R., C. Shih, W. Webster, and R. Card, "Enhanced Inspection and
Maintenance Program: An Investigation of Real Time Data Utility and Dynamic
Quality Assurance Instrumentation - Task 1: A Predictive Modal Regression
Methodology for IM240 Fast-Pass/Fail Decisions," New York State Department of
Environmental Conservation, EPA Cooperative Agreement #X992008-01-0,
November 29, 1996.
5.	"Analysis of Alternate IM240 Cutpoints, Phase 2 Testing, and Exempting New
Vehicle Models on Test Duration and Projected I/M Benefits," Prepared by Sierra
Research for the Arizona Department of Environmental Quality, Report No. SR98-
05-01,May 12, 1998.
6.	"High-Tech I/M Test Procedures, Emission Standards, Quality Control Requirements,
and Equipment Specifications: IM240 and Functional Evaporative System Tests,"
(Revised Draft), U.S. Environmental Protection Agency, EPA-AA-RSPD-IM-96-1,
June 1996.
7.	W. J. Webster and C. Shih, "A Statistically Derived Metric to Monitor Time-Speed
Variability in Practical Emissions Testing," New York State Department of
Environmental Conservation, presented at the 6th CRC On-Road Vehicle Emissions
Workshop, March 18-20, 1996.
###
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Appendix A
Startup IM240 Cutpoints and IM147 Cutpoints*
(Composite/Phase 2 Cutpoints in g/mi, IM240 - IM147)
Model Years
HC
CO
NOx
LDGV
1981-82
2.00/1.25 - 2.00/1.20
60.0/48.0 - 58.0/30.0
3.0-3.3/1.2
1983-85
2.00/1.25 -2.00/1.20
30.0/24.0 - 30.0/15.0
3.0-3.3/1.2
1986-90
2.00/1.25 - 2.00/1.00
30.0/24.0- 30.0/10.0
3.0-3.0/1.2
1991-93
1.20/0.75 - 1.30/0.60
20.0/16.0-21.0/10.0
2.5 - 2.9/1.0
1994-95
1.20/0.75 - 1.20/0.60
20.0/16.0-21.0/10.0
2.5-2.7/1.0
1996+ (Tier 1)
0.80/0.50 - 0.80/0.50
15.0/12.0- 15.0/7.0
2.0-2.1/0.9
LDGT1
1981-83
7.50/5.00 - 6.70/4.70
100.0/80.0 - 95.0/50.0
7.0-7.6/2.9 .
1984-85
3.20/2.00-2.90/2.00
80.0/64.0 - 76.0/40.0
7.0 - 7.6/2.9
1986-87
3.20/2.00-2.90/1.60
80.0/64.0- 76.0/31.0
7.0 - 7.0/2.7
1988-90
3.20/2.00-2.90/1.60
80.0/64.0- 76.0/31.0
3.5-3.6/1.3
1991-93
2.40/1.50-2.60/1.20
60.0/48.0- 61.0/31.0
3.0-3.4/1.1
1994-95
2.40/1.50-2.40/1.20
60.0/48.0-61.0/29.0
3.0-3.2/1.1
1996+(Tier 1)
1.00/0.63 - 1.0/0.60
20.0/16.0-21.0/10.0
2.5-2.7/1.1
LDGT2
1981-83
7.50/5.00 - 6.70/4.70
100.0/80.0 - 95.0/50.0
7.0-7.6/2.9
1984-86 .
3.20/2.00-2.90/2.00
80.0/64.0 - 76.0/40.0
7.0 - 7.6/2.9
1987
3.20/2.00-2.90/1.60
80.0/64.0- 76.0/31.0
7.0 - 7.6/2.7
1988-90
3.20/2.00-2.90/1.60
80.0/64.0- 76.0/31.0
5.0-5.1/1.9
1991-93
2.40/1.50-2.60/1.20
60.0/48.0- 61.0/31.0
4.5-5.1/1.9
1994-95
2.40/1.50-2.40/1.20
60.0/48.0- 61.0/29.0
4.5-4.8/1.9
1996+(Tier 1)
2.40/1.50-2.40/1.20
60.0/48.0- 61.0/29.0
4.0 - 4.3/1.7
Developed for SR99-10-02
A-l

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Intermediate IM240 Cutpoints and IM147 Outpoints Developed in This Study
(Composite/Phase 2 Cutpoints in g/mi, IM240 - IM147)
Model Years
HC
CO
NOx
LDGV
1981-82
1.40/0.88 - 1.40/0.90
45.0/36.0-44.0/23.0
2.3-2.8/1.0
1983-85
1.40/0.88 - 1.40/0.90
23.0/18.0-23.0/12.0
. 2.3-2.8/1.0
1986-90
1.40/0.88 - 1.40/0.70
23.0/18.0-23.0/9.0
2.3-2.6/1.0
1991-93
1.00/0.63 - 1.10/0.50
18.0/14.0- 18.0/9.0
2.3 - 2.6/0.9
1994-95
1.00/0.63 - 1.00/0.50
18.0/14.0- 18.0/9.0
2.3 - 2.5/0.9
1996+ (Tier 1)
0.70/0.45 - 0.80/0.40
13.0/10.0- 15.0/6.0
1.8-2.2/0.8
LDGT1
1981-83
5.50/3.50-4.90/3.40
85.0/68.O-81.0/43.0
5.8 - 6.3/2.4
1984-85
2.40/1.50 - 2.30/1.50
60.0/48.0 - 60.0/30.0
5.8 - 6.3/2.4
1986-87
2.40/1.50 - 2.30/1.20
60.0/48.0 - 60.0/26.0
5.8-5.8/2.2
1988-89
2.40/1.50-2.30/1.20
60.0/48.0 - 60.0/26.0
3.0-3.3/1.2
1990
2.40/1.50-2.30/1.20
60.0/48.0 - 59.0/26.0
3.0 - 3.3/1.2
1991-93
2.00/1.25 -2.10/1.00
50.0/40.0 - 51.0/26.0
2.8-3.2/1.1
1994-95
2.00/1.25-2.00/1.00
50.0/40.0- 51.0/25.0
2.8-3.0/1.1
1996+ (Tier 1)
0.90/0.57- 1.30/0.60
17.0/13.0-31.0/8.0
2.2-2.7/1.0
LDGT2
1981-83
5.50/3.50 - 4.90/3.40
85.0/68.0-81.0/43.0
5.8 - 6.3/2.4
1984-86
2.40/1.50-2.30/1.50
60.0/48.0 - 60.0/30.0
5.8 - 6.3/2.4
1987
2.40/1.50-2.30/1.20
60.0/48.0 - 60.0/26.0
5.8-6.3/2.2
1988-90
2.00/1.50 - 2.30/1.20
60.0/48.0 - 60.0/26.0
4.3-4.6/1.6
1991-93
2.00/1.25 -2.20/1.00
50.0/40.0-51.0/26.0
4.0-4.6/1.6
1994-95
2.00/1.25 -2.00/1.00
50.0/40.0-51.0/25.0
4.0-4.3/1.6
1996+ (Tier 1)
.1.60/1.00-2.00/0.90
38.0/30.0- 51.0/18.0
3.0-4.1/1.3
A-2

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Final IM240 Cutpoints and IM147 Cutpoints Developed in This Study
(Composite/Phase 2 Cutpoints in g/mi, IM240 - IM147)
Model Years
HC
CO
NOx
LDGV
1981-82
0.80/0.50-0.80/0.50
30.0/24.0-30.0/15.0
2.0 - 2.3/0.8
1983-85
0.80/0.50 - 0.80/0.50
15.0/12.0 - 16.0/8.0
2.0-2.3/0.8
1986-89
0.80/0.50 - 0.80/0.40
15.0/12.0 - 16.0/8.0
2.0 - 2.2/0.8
1990-93
0.80/0.50 - 0.80/0.50
15.0/12.0- 15.0/8.0
2.0 - 2.2/0.7
1994-95
0.80/0.50 - 0.80/0.50
15.0/12.0 - 15.0/7.0
2.0 - 2.2/0.7
1996+ (Tier 1)
0.60/0.40 -0.80/0.30
10.0/8.0- 15.0/5.0
1.5-2.2/0.6
LDGT1
1981-83
3.40/2.00-3.10/2.10
70.0/56.0- 67.0/35.0
4.5 - 4.9/1.8
1984-85
1.60/1.00- 1.70/1.00
40.0/32.0 - 43.0/20.0
4.5-4.9/1.8
1986-87
1.60/1.00- 1.70/0.80
40.0/32.0 - 43.0/20.0
4.5-4.6/1.7
1988-89
1.60/1.00 - 1.70/0.80
40.0/32.0-43.0/20.0
2.5-2.9/1.0
 1990-93
1.60/1.00- 1.60/0.80
40.0/32.0-41.0/20.0
2.5-2.9/1.0
1994-95
1.60/1.00- 1.60/0.80
40.0/32.0-41.0/20.0
2.5-2.7/1.0
1996+ (Tier 1)
0.80/0.50- 1.60/0.50
13.0/10.0-41.0/6.0
oo
NJ

CO
LDGT2
1981-83
3.40/2.00-3.10/2.10
70.0/56.0 - 67.0/35.0
4.5-4.9/1.8
1984-86
1.60/1.00- 1.70/1.00
40.0/32.0 - 43.0/20.0
4.5-4.9/1.8
1987
1.60/1.00- 1.70/0.80
40.0/32.0 - 43.0/20.0
4.5-4.9/1.7
1988-91
1.60/1.00- 1.70/0.80
40.0/32.0 -43.0/20.0
3.5-4.0/1.3
1992-93
1.60/1.00- 1.70/0.80
40.0/32.0-41.0/20.0
3.5-4.0/1.3
1994-95
1.60/1.00- 1.70/0.80
40.0/32.0-41.0/20.0
3.5-3.8/1.3
1996+ (Tier 1)
0.80/0.50- 1.60/0.50
15.0/12.0-41.0/7.0
2.0-3.8/0.9
A-3

-------
Appendix B
Failure Rate
Max CO Cutpoints

HC
CO
NOx
OVERALL
V Type
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
First IM147
LDGV
207
1573
11.6%
352
1428
19.8%
236
1544
13.3%
543
1237
30.5%
LDGT1
76
888
7.9%
204
760
21.2%
83
881
8.6%
281
683
29.1%
LDGT2
31
572
5.1%
87
516
14.4%
44
559
7.3%
127
476
21.1%
All
314
3033
9.4%
643
2704
19.2%
363
2984
10.8%
951
2396
28.4%
Second IM147
LDGV
126
1654
7.1%
256
1524
14.4%
166
1614
9.3%
406
1374
22.8%
LDGT1
59
905
6.1%
167
797
17.3%
70
894
7.3%
231
733
24.0%
LDGT2
24
579
4.0%
61
542
10.1%
31
572
5.1%
91
512
15.1%
All
209
3138
6.2%
484
2863
14.5%
267 ,
3080
8.0%
728
2619
21.8%
Third IM147
LDGV
114
1666
6.4%
250
1530
14.0%
155
1625
8.7%
390
1390
21.9%
LDGT1
49
915
5.1%
156
808
16.2%
70
894
7.3%
219
745
22.7%
LDGT2
25
578
4.1%
63
540
10.4%
30
573
5.0%
93
510
15.4%
All
188
3159
5.6%
469
2878
14.0%
255
3092
7.6%
702
2645
21.0%
Failure Rate By Model Year Grouping - First IM147
Max CO Cutpoints


HC
CO
NOx
OVERALL
V Type
Year
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
LDGV
81-82
19
86
18.1%
32
73
30.5%
24
81
22.9%
55
50
52.4%
83-85
48
180
21.1%
108
120
47.4%
53
175
23.2%
144
84
63.2%
86-89
85
340
20.0%
96
329
22.6%
87
338
20.5%
169
256
39.8%
90-95
55
897
5.8%
112
840
11.8%
72
880
7.6%
171
781
18.0%
96+
0
70
0.0%
4
66
5.7%
0
70
0.0%
4
66
5.7%
LDGT1
81-85
38
222
14.6%
109
151
41.9%
54
206
20.8%
152
108
58.5%
86-89
22
200
9.9%
57
165
25.7%
17
205
7.7%
73
149
32.9%
90-95
16
434
3.6%
38
412
8.4%
11
439
2.4%
55
395
12.2%
96+
0
32
0.0%
0
32
0.0%
1
31
3.1%
1
31
3.1%
LDGT2
81-85
18
76
19.1%
44
50
46.8%
16
78
17.0%
55
39
58.5%
86-89
8
56
12.5%
13
51
20.3%
13
51
20.3%
27
37
42.2%
88-95
5
422
1.2%
30
397
7.0%
15
412
3.5%
45
382
10.5%
96+
0
18
0.0%
0
18
0.0%
0
18
0.0%
0
18
0.0%

ALL
314
3033
9.4%
643
2704
19.2%
363
2984
10.8%
951
2396
28.4%
B-l

-------
Failure Rate By Model Year Grouping - Second IM147
Max CO Cutpoints
V Type
Year
HC
CO
NOx
OVERALL
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
LDGV
81-82
13
92
12.4%
29
76
27.6%
18
87
17.1%
49
56
46.7%
83-85
32
196
14.0%
81
147
35.5%
43
185
18.9%
116
112
50.9%
86-89
57
368
13.4%
82
343
19.3%
67
358
15.8%
141
284
33.2%
90-95
24
928
2.5%
64
888
6.7%
38
914
4.0%
100
852
10.5%
96+
0
70
0.0%
0
70
0.0%
0
70
0.0%
0
70
0.0%
LDGT1
81-85
33
227
12.7%
99
161
38.1%
48
212
18.5%
139
121
53.5%
86-89
19
203
8.6%
48
174
21.6%
10
212
4.5%
59
163
26.6%
90-95
7
443
1.6%
20
430
4.4%
11
439
2.4%
32
418
7.1%
96+
0
32
0.0%
0
32
0.0%
1
31
3.1%
1
31
3.1%
LDGT2
81-85
16
78
17.0%
40
54
42.6%
14
80
14.9%
53
41
56.4%
86-89
5
59
7.8%
11
53
17.2%
11
53
17.2%
22
42
34.4%
88-95
3
424
0.7%
10
417
2.3%
6
421
1.4%
16
411
3.7%
96+
0
18
0.0%
0
18
0.0%
0
18
0.0%
0
18
0.0%

ALL
209
3138
6.2%
484
2863
14.5%
267
3080
8.0%
728
2619
21.8%
Failure Rate By Model Year Grouping - Third IM147
Max CO Cutpoints
V Type
Year
HC
CO
NOx
OVERALL
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
LDGV
81-82
13
92
12.4%
29
76
27.6%
16
89
15.2%
46
59
43.8%
83-85
29
199
12.7%
79
149
34.6%
39
189
17.1%
110
118
48.2%
86-89
48
377
11.3%
77
348
18.1%
65
360
15.3%
137
288
32.2%
90-95
24
928
2.5%
64
888
6.7%
35
917
3.7%
96
856
10.1%
96+
0
70
0.0%
1
69
1.4%
0
70
0.0%
1
69
1.4%
LDGT1
81-85
31
229
11.9%
95
165
36.5%
47
213
18.1%
134
126
51.5%
86-89
15
207
6.8%
43
179
19.4%
12
210
5.4%
56
166
25.2%
90-95
3
447
0.7%
18
432
4.0%
10
440
2.2%
28
422
6.2%
96+
0
32
0.0%
0
32
0.0%
1
31
3.1%
1
31
3.1%
LDGT2
81-85
16
78
17.0%
40
54
42.6%
13
81
13.8%
53
41
56.4%
86-89
6
58
9.4%
13
51
20.3%
10
54
15.6%
23
41
35.9%
88-95
3
424
0.7%
10
417
2.3%
7
420
1.6%
17
410
4.0%
96+
0
18
0.0%
0
18
0.0%
0
18
0.0%
0
18
0.0%

ALL
188
3159
5.6%
469
2878
14.0%
255
3092
7.6%
702
2645
21.0%
B-2

-------
Failure Rate
Startup Cutpoints
V Type
HC
CO
NOx
OVERALL
Fail Pass % Fail
Fail | Pass | % Fail
Fail | Pass % Fail
Fail Pass % Fail
First IM147
LDGV
180
1600
10.1%
179
1601
10.1%
216
1564
12.1%
416
1364
23.4%
LDGT1
75
889
7.8%
37
927
3.8%
84
880
8.7%
166
798
17.2%
LDGT2
56
547
9.3%
37
566
6.1%
45
558
7.5%
103
500
17.1%
All
311
3036
9.3%
253
3094
7.6%
345
3002
10.3%
685
2662
20.5%
Second IM147
LDGV
109
1671
6.1%
133
1647
7.5%
152
1628
8.5%
291
1489
16.3%
LDGT1
51
913
5.3%
40
924
4.1%
51
913
5.3%
115
849
11.9%
LDGT2
32
571
5.3%
31
572
5.1%
34
569
5.6%
74
529
12.3%
All
192
3155
5.7%
204
3143
6.1%
237
3110
7.1%
480
2867
14.3%
Third IM147
LDGV
106
1674
6.0%
130
1650
7.3%
141
1639
7.9%
281
1499
15.8%
LDGT1
52
912
5.4%
33
931
3.4%
48
916
5.0%
108
856
11.2%
LDGT2
33
570
5.5%
30
573
5.0%
28
575
4.6%
67
536
11.1%
All
191
3156
5.7%
193
3154
5.8%
217
3130
6.5%
456
2891
13.6%
Failure Rate By Model Year Grouping - First IM147
Startup Cutpoints
V Type
Year
HC
CO
NOx
OVERALL
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
LDGV
81-82
30
75
28.6%
13
92
12.4%
24
81
22.9%
48
57
45.7%
83-85
56
172
24.6%
75
153
32.9%
49
179
21.5%
122
106
53.5%
86-89
57
368
13.4%
50
375
11.8%
72
353
16.9%
128
297
30.1%
90-95
36
916
3.8%
37
915
3.9%
71
881
7.5%
114
838
12.0%
96+
1
69
1.4%
4
66
5.7%
0
70
0.0%
4
66
5.7%
LDGT1
81-85
44
216
16.9%
24
236
9.2%
21
239
8.1%
74
186
28.5%
86-89
24
198
10.8%
11
211
5.0%
29
193
13.1%
53
169
23.9%
90-95
7
443
1.6%
2
448
0.4%
32
418
7.1%
37
413
8.2%
96+
0
32
0.0%
0
32
0.0%
2
30
6.3%
2
30
6.3%
LDGT2
81-85
29
65
30.9%
26
68
27.7%
9
85
9.6%
41
53
43.6%
86-89
14
50
21.9%
6
58
9.4%
7
57
10.9%
20
44
31.3%
88-95
13
414
3.0%
5
422
1.2%
29
398
6.8%
42
385
9.8%
96+
0
18
0.0%
0
18
0.0%
0
18
0.0%
0
18
0.0%

ALL
311
3036
9.3%
253
3094
7.6%
345
3002
10.3%
685
2662
20.5%
B-3

-------
Failure Rate By Model Year Grouping - Second IM147
Startup Outpoints
V Type
Year
HC
CO
NOx
OVERALL
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
LDGV
81-82
24
81
22.9%
13
92
12.4%
18
87
17.1%
38
67
36.2%
83-85
35
193
15.4%
53
175
23.2%
43
185
18.9%
92
136
40.4%
86-89
36
389
8.5%
42
383
9.9%
55
370
12.9%
101
324
23.8%
90-95
14
938
1.5%
25
927
2.6%
36
916
3.8%
60
892
6.3%
96+
0
70
0.0%
0
70
0.0%
0
70
0.0%
0
70
0.0%
LDGT1
81-85
31
229
11.9%
23
237
8.8%
18
242
6.9%
58
202
22.3%
86-89
18
204
8.1%
14
208
6.3%
18
204
8.1%
39
183
17.6%
90-95
2
448
0.4%
3
447
0.7%
14
436
3.1%
17
433
3.8%
96+
0
32
0.0%
0
32
0.0%
1
31
3.1%
1
31
3.1%
LDGT2
81-85
20
74
21.3%
23
71
24.5%
9
85
9.6%
.35
59
37.2%
86-89
8
56
12.5%
5
59
7.8%
8
56
12.5%
16
48
25.0%
88-95
4
423
0.9%
3
424
0.7%
17
410
4.0%
23
404
5.4%
96+
0
18
0.0%
0
18
0.0%
0
18
0.0%
0
18
0.0%

ALL
192
3155
5.7%
204
3143
6.1%
237
3110
7.1%
480
2867
14.3%
Failure Rate By Model Year Grouping - Third IM147
Startup Outpoints
V Type
Year
HC
CO
NOx
OVERALL
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
LDGV
81-82
23
82
21.9%
13
92
12.4%
14
91
13.3%
35
70
33.3%
83-85
33
195
14.5%
56
172
24.6%
39
189
17.1%
90
138
39.5%
86-89
35
390
8.2%
36
389
8.5%
56
369
13.2%
96
329
22.6%
90-95
15
937
1.6%
24
928
2.5%
32
920
3.4%
59
893
6.2%
96+
0
70
0.0%
1
69
1.4%
0
70
0.0%
1
69
1.4%
LDGT1
81-85
34
226
13.1%
20
240
7.7%
17
243
6.5%
56
204
21.5%
86-89
17
205
7.7%
12
210
5.4%
16
206
7.2%
35
187
15.8%
90-95
1
449
0.2%
1
449
0.2%
14
436
3.1%
16
434
3.6%
96+
0
32
0.0%
0
32
0.0%
1
31
3.1%
1
31
3.1%
LDGT2
81-85
21
73
22.3%
22
72
23.4%
7
87
7.4%
33
61
35.1%
86-89
9
55
14.1%
6
58
9.4%
7
57
10.9%
16
48
25.0%
88-95
3
424
0.7%
2
425
0.5%
14
413
3.3%
18
409
4.2%
96+
0
18
0.0%
0
18
0.0%
0 '
18
0.0%
0
18
0.0%

ALL
191
3156
5.7%
193
3154
5.8%
217
3130
6.5%
456
2891
13.6%
B-4

-------
Failure Rate
Intermediate Cut
loints
V Type
HC
CO
NOx
OVERALL
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
First IM147
LDGV
286
1494
16.1%
238
1542
13.4%
281
1499
15.8%
533
1247
29.9%
LDGT1
121
843
12.6%
62
902
6.4%
122
842
12.7%
247
717
25.6%
LDGT2
84
519
13.9%
47
556
7.8%
71
532
11.8%
142
461
23.5%
All
491
2856
14.7%
347
3000
10.4%
474
2873
14.2%
922
2425
27.5%
Second IM147
LDGV
170
1610
9.6%
161
1619
9.0%
214
1566
12.0%
386
1394
21.7%
LDGT1
87
877
9.0%
57
907.
5.9%
78
886
8.1%
176
788
18.3%
LDGT2
46
557
7.6%
40
563
6.6%
48
555
8.0%
100
503
16.6%
All
303
3044
9.1%
258
3089
7.7%
340
3007
10.2%
662
2685
19.8%
Third IM147
LDGV
157
1623
8.8%
161
1619
9.0%
196
1584
11.0%
367
1413
20.6%
LDGT1
80
884
8.3%
53
911
5.5%
73
891
7.6%
165
799
17.1%
LDGT2
40
563
6.6%
42
561
7.0%
42
561
7.0%
93
510
15.4%
All
277
3070
8.3%
256
3091
7.6%
311
3036
9.3%
625
2722
18.7%
Failure Rate By Model Year Grouping - First IM147
Intermediate Outpoints
V Type
Year
HC
CO
NOx
OVERALL
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
LDGV
81-82
52
53
49.5%
20
85
19.0%
27
78
25.7%
64
41
61.0%
83-85
88
140
38.6%
91
137
39.9%
64,
164
28.1%
146
82
64.0%
86-89
94
331
22.1%
65
360
15.3%
100
325
23.5%
169
256
39.8%
90-95
51
901
5.4%
58
894
6.1%
90
862
9.5%
150
802
15.8%
96+
1
69
1.4%
4
66
5.7%
0
70
0.0%
4
66
5.7%
LDGT1
81-85
71
189
27.3%
42
218
16.2%
37
223
14.2%
117
143
45.0%
86-89
36
186
16.2%
14
208
6.3%
39
183
17.6%
71
151
32.0%
90-95
14
436
3.1%
6
444
1.3%
44
406
9.8%
57
393
12.7%
96+
0
32
0.0%
0
32
0.0%
2
30
6.3%
2
30
6.3%
LDGT2
81-85
37
57
39.4%
32
62
34.0%
17
77
18.1%
53
41
56.4%
86-89
19
45
29.7%
9
55
14.1%
10
54
15.6%
24
40
37.5%
88-95
28
399
6.6%
6
421
1.4%
44
383
10.3%
65
362
15.2%
96+
0
18
0.0%
0
18
0.0%
0
18
0.0%
0
18
0.0%

ALL
491
2856
14.7%
347
3000
10.4%
474
2873
14.2%
922
2425
27.5%
B-5

-------
Failure Rate By Model Year Grouping - Second IM147
Intermediate Cutpoints
V Type
Year
HC
CO
NOx
OVERALL
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
LDGV
81-82
36
69
34.3%
15
90
14.3%
23
82
21.9%
49
56
46.7%
83-85
51
177
22.4%
66
162
28.9%
60
168
26.3%
118
110
51.8%
86-89
61
364
14.4%
50
375
11.8%
76
349
17.9%
134
291
31.5%
90-95
22
930
2.3%
30
922
3.2%
55
897
5.8%
85
867
8.9%
96+
0
70
0.0%
0
70
0.0%
0
70
0.0%
0
70
0.0%
LDGT1
81-85
57
203
21.9%
34
226
13.1%
31
229
11.9%
95
165
36.5%
86-89
24
198
10.8%
18
204
8.1%
25
197
11.3%
52
170
23.4%
90-95
6
444
1.3%
5
445
1.1%
21
429
4.7%
28
422
6.2%
96+
0
32
0.0%
0
32
0.0%
1
31
3.1%
1
31
3.1%
LDGT2
81-85
29
65
30.9%
32
62
34.0%
15
79
16.0%
50
44
53.2%
86-89
10
54
15.6%
5
59
7.8%
11
53
17.2%
19
45
29.7%
88-95
7
420
1.6%
3
424
0.7%
22
405
5.2%
31
396
7.3%
96+
0
18
0.0%
. 0
18
0.0%
0
18
0.0%
0
18
0.0%

ALL
303
3044
9.1%
258
3089
7.7%
340
3007
10.2%
662
2685
19.8%
Failure Rate By Model Year Grouping - Third IM147
Intermediate Cutpoints
V Type
Year
HC
CO
NOx
OVERALL
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
LDGV
81-82
34
71
32.4%
16
89
15.2%
20
85
19.0%
48
57
45.7%
83-85
48
180
21.1%
66
162
28.9%
58
170
25.4%
113
115
49.6%
86-89
56
369
13.2%
49
376
11.5%
72
353
16.9%
128
297
30.1%
90-95
19
933
2.0%
29
923
3.0%
46
906
4.8%
77
875
8.1%
96+
0
70
0.0%
1
69
1.4%
0
70
0.0%
1
69
1.4%
LDGT1
81-85
56
204
21.5%
34
226
13.1%
29
231
11.2%
92
168
35.4%
86-89
21
201
9.5%
16
206
7.2%
25
197
11.3%
50
172
22.5%
90-95
3
447
0.7%
3
447
0.7%
18
432
4.0%
22
428
4.9%
96+
0
32
0.0%
0
32
0.0%
1
31
3.1%
1
31
3.1%
LDGT2
81-85
25
69
26.6%
31
63
33.0%
14
80
14.9%
47
47
50.0%
86-89
10
54
15.6%
8
56
12.5%
8
56
12.5%
19
. 45
29.7%
88-95
5
422
1.2%
3
424
0.7%
20
407
4.7%
27
400
6.3%
96+
0
18
0.0%
0
18
0.0%
0
18
0.0%
0
18
0.0%

ALL
277
3070
8.3%
256
3091
7.6%
311
3036
9.3%
625
2722
18.7%
B-6

-------
Failure Rate
Final Outpoints
V Type
HC
CO
NOx
OVERALL
Fail Pass % Fail
Fail Pass | % Fail
Fail | Pass % Fail
Fail Pass % Fail
First IM147
LDGV
499
1281
28.0%
346
1434
19.4%
396
1384
22.2%
733
1047
41.2%
LDGT1
210
754
21.8%
109
855
11.3%
184
780
19.1%
348
616
36.1%
LDGT2
135-
468
22.4%
66
537
10.9%
120
483
19.9%
219
384
36.3%
All
844
2503
25.2%
521
2826
15.6%
700
2647
20.9%
1300
2047
38.8%
Second IM147
LDGV
309
1471
17.4%
247
1533
13.9%
296
1484
16.6%
539
1241
30.3%
LDGT1
137
827
14.2%
91
873
9.4%
134
830
13.9%
256
708
26.6%
LDGT2
77
526
12.8%
56
547
9.3%
88
515
14.6%
157
446
26.0%
All
523
2824
15.6%
394
2953
11.8%
518
2829
15.5%
952
2395
28.4%
Third IM147
LDGV
280
1500
15.7%
240
1540
13.5%
277
1503
15.6%
507
1273
28.5%
LDGT1
120
844
12.4%
85
879
8.8%
127
837
13.2%
239
725
24.8%
LDGT2
73
530
12.1%
56
547
9.3%
82
521
13.6%
149
454
24.7%
All
473
2874
14.1%
381
2966
11.4%
486
2861
14.5%
895
2452
26.7%
Failure Rate By Model Year Grouping - First IM147
Final Cutpoints
V Type
Year
HC
CO
NOx
OVERALL
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
LDGV
81-82
70
35
66.7%
30
75
28.6%
39
66
37.1%
78
27
74.3%
83-85
148
80
64.9%
120
108
52.6%
81
147
35.5%
183
45
80.3%
86-89
179
246
42.1%
102
323
24.0%
144
281
33.9%
247
178
58.1%
90-95
100
852
10.5%
90
862
9.5%
132
820
13.9%
220
732
23.1%
96+
2
68
2.9%
4
66
5.7%
0
70
0.0%
5
65
7.1%
LDGT1
81-85
116
144
44.6%
69
191
26.5%
70
190
26.9%
165
95
63.5%
86-89
71-
151
32.0%
29
193
13.1%
53
169
23.9%
104
118
46.8%
90-95
23
427
5.1%
11
439
2.4%
59
391
13.1%
77
373
17.1%
96+
0
32
0.0%
0
32
0.0%
2
30
6.3%
2
30
6.3%
LDGT2
81-85
50
44
53.2%
41
53
43.6%
32
62
34.0%
73
21
77.7%
86-89
29
35
45.3%
14
50
21.9%
20
44
31.3%
42
22
65.6%
88-95
56
371
13.1%
11
416
2.6%
68
359
15.9%
104
323
24.4%
96+
0
18
0.0%
0
18
0.0%
0
18
0.0%
0
18
0.0%

ALL
844
2503
25.2%
521
2826
15.6%
700
2647
20.9%
1300
2047
38.8%
B-7

-------
Failure Rate By Model Year Grouping - Second IM147
Final Outpoints
V Type
Year
HC
CO
NOx
OVERALL
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
LDGV
81-82
56
49
53.3%
26
79
24.8%
37
68
35.2%
74
31
70.5%
83-85
104
124
45.6%
91
137
39.9%
79
149
34.6%
157
71
68.9%
86-89
110
315
25.9%
83
342
19.5%
101
324
23.8%
181
244
42.6%
90-95
39
913
4.1%
47
905
4.9%
79
873
8.3%
127
825
13.3%
96+
0
70
0.0%
0
70
0.0%
0
70
0.0%
0
70
0.0%
LDGT1
81-85
90
170
34.6%
63
197
24.2%
65
195
25.0%
145
115
55.8%
86-89
36
186
16.2%
22
200
9.9%
39
183
17.6%
69
153
31.1%
90-95
11
439
2.4%
6
444
1.3%
29
.421
6.4%
41
409
9.1%
96+
0
32
0.0%
0
32
0.0%
1
31
3.1%
1
31
3.1%
LDGT2
81-85
46
48
48.9%
41
53
43.6%
31
63
33.0%
72
22
76.6%
86-89
14
50
21.9%
12
52
18.8%
18
46
28.1%
33
31
51.6%
88-95
17
410
4.0%
3
424
0.7%
39
388
9.1%
52
375
12.2%
96+
0
18
0.0%
0
18
0.0%
0
18
0.0%
0
18
0.0%

ALL
523
2824
15.6%
394
2953
11.8%
518
2829
15.5%
952
2395
28.4%
Failure Rate By Model Year Grouping - Third IM147
Final Cutpoints
V Type
Year
HC
CO
NOx
OVERALL
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
Fail
Pass
% Fail
LDGV
81-82
53
52
50.5%
25
80
23.8%
32
73
30.5%
67
38
63.8%
83-85
91
137
39.9%
88
140
38.6%
81
147
35.5%
156
72
68.4%
86-89
100
325
23.5%
76
349
17.9%
96
329
22.6%
171
254
40.2%
90-95
36
916
3.8%
50
902
5.3%
68
884
7.1%
112
840
11.8%
96+
0
70
0.0%
1
69
1.4%
0
70
0.0%
1
69
1.4%
LDGT1
81-85
83
177
31.9%
57
203
21.9%
61
199
23.5%
137
123
52.7%
86-89
31
191
14.0%
24
198
10.8%
36
186
16.2%
65
157
29.3%
90-95
6
444
1.3%
4
446
0.9%
29
421
6.4%
36
414
8.0%
96+
0
32
0.0%
0
32
0.0%
1
31
3.1%
1
31
3.1%
LDGT2
81-85
44
50
46.8%
41
53
43.6%
31
63
33.0%
.73
21
77.7%
86-89
16
48
25.0%
11
53
17.2%
17
47
26.6%
32
32
50.0%
88-95
13
414
3.0%
4
423
0.9%
34
393
8.0%
44
383
10.3%
96+
0
18
0.0%
0
18
0.0%
0
18
0.0%
0
18
0.0%

ALL
473
2874
14.1%
381
2966
11.4%
486
2861
14.5%
895
2452
26.7%
B-8

-------
APPENDIX C
1
M147 Composite Repression Coefficients, HC, 1981 to 1985 Model Year LDGH's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT1
81-85
0.73806
0.95039
13.5112


















P2
LDGT1
81-85
0.52814
0.34501
2.7265
5.5143

















P3
LDGT1
81-85
0.40889
0.23152
0.5547
3.2006
6.2124
















P4
LDGT1
81-85
0.38717
0.22535
-1.0106
3.4095
3.2694
3.5979















P5
LDGT1
81-85
0.36872
0.23976
-0.8175
2.5639
3.1218
2.0148
3.4755














P6
LDGT1
81-85
0.34978
0.25562
-2.8842
2.7609
1.8633
1.5365
2.3446
3.2626













P7
LDGT1
81-85
0.33929
0.24395
-2.5969
2.4215
2.0512
1.644
0.5818
2.2896
2.9199












P8
LDGT1
81-85
0.30707
0.21404
-0.9821
1.8007
1.4784
1.745
0.0989
0.8955
2.4381
4.82











P9
LDGT1
81-85
0.30661
0.21485
-0.9303
1.7873
1.3395
1.7754
0.0281
0.6238
2.4791
4.5115
0.8987










P10
LDGT1
81-85
0.30246
0.21468
-1.189
1.8114
1.1577
2.1188
-0.3285
0.5235
2.6637
4.255
-0.5878
2.1284









P11
LDGT1
81-85
0.25475
0.05761
-0.2293
0.7411
1.0904
1.5866
0.0582
1.3727
1.9974
2.4699
0.822
1.4541
1.8008








P12
LDGT1
81-85
0.24251
0.05503
-0.1605
0.7753
0.9307
1.635
0.0664
1.336
2.0956
2.1337
0.2754
2.1379
1.2063
1.1106







P13
LDGT1
81-85
0.19345
0.05879
1.3617
0.2166
0.9329
1.5508
-0.1316
1.2355
2.3977
1.6874
0.0101
0.9701
0.9488
-0.4982
3.2351






P14
LDGT1
81-85
0.1631
0.04219
1.279
0.1384
1.1759
1.4082
0.0948
1.1156
2.0131
1.2938
0.729
1.1342
0.4477
-0.3274
1.562
4.0745





P15
LDGT1
81-85
0.13928
0.03346
1.8241
0.0799
1.2164
1.2299
0.2827
0.8749
2.2275
0.6681
0.8755
0.7239
0.5659
0.1723
0.7424
1.1987
2.7671




P16
LDGT1
81-85
0.13703
0.03021
1.8786
0.023
1.2823
1.1806
0.305
0.8514
2.2773
0.5758
0.8491
0.7433
0.5775
0.2633
0.6459
1.2728
2.0331
2.1897



P17
LDGT1
81-85
0.09132
0.00475
1.4179
0.434
0.6904
0.7838
1.0571
0.616
1.5938
0.7518
0.2064
0.8618
0.5896
0.4328
0.7951
1.3837
1.3066
0.6628
2.7633


P18
LDGT1
81-85
0.07314
-0.00235
1.5086
0.5696
0.7494
0.8068
0.8363
0.5804
1.3972
0.5907
0.4057
0.6221
0.5658
0.584
0.7038
0.8587
0.988
0.3277
1.9729
1.7883

P19
LDGT1
81-85
0.06979
-0.00621
1.375
0.6281
0.6996
0.852
0.9084
0.5029
1.4414
0.5441
0.3942
0.5659
0.6012
0.6081
0.7093
0.7313
0.6296
0.2443
1.7266
1.5869
0.729
IM147 Phase 2 Regression Coefficients, HC, 1981 to 1985 Model Year LDGH's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT1
81-85
0.5023
0.12381










4.4447








B12
LDGT1
81-85
0.48187
0.11013










3.3244
1.9633







B13
LDGT1
81-85
0.35264
0.07757










1.7904
-1.1796
5.9021






B14
LDGT1
81-85
0.32195
0.06184










0.9978
-1.0185
3.8122
5.5161





B15
LDGT1
81-85
0.2826
0.04064










1.0928
0.0499
1.7408
0.7784
4.7049




B16
LDGT1
81-85
0.27957
0.0348










1.0816
0.2105
1.5379
0.9523
3.4207
3.6479



B17
LDGT1
81-85
0.14692
-0.01627










1.0035
0.5208
1.1766
2.2402
1.4703
0.4909
4.6919


B18
LDGT1
81-85
0.11692
-0.02022










0.9177
0.8016
0.9222
1.5324
0.8911
0.0868
3.252
2.7658

B19
LDGT1
81-85
0.11286
-0.02299










0.9799
0.8216
0.9206
1.4305
0.4043
0.0294
2.8156
2.4898
0.9698

-------
1
M147 Composite Regression Coefficients, HC, 1986 to 1989 Model Year LDGH's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
I
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT1
86-89
0.49429
0.5423
17.2823


















P2
LDGT1
86-89
0.37436
0.2461
2.2738
4.5192

















P3
LDGT1
86-89
0.33313
0.22224
0.5252
2.5885
4.6599
















P4
LDGT1
86-89
0.29595
0.20426
0.5943
2.3293
2.3375
3.7634















P5
LDGT1
86-69
0.2938
0.19895
0.6763
2.0377
2.3025
2.9749
1.8038














P6
LDGT1
86-89
0.28058
0.20644
0.6191
2.2969
1.2069
2.4549
-0.1084
4.2386













P7
LDGT1
86-69
0.27458
0.19491
-0.0117
2.0167
1.3562
2.7382
-1.5023
2.6456
2.8231


.









P8
LDGT1
86-89
0.26273
0.17858
0.9768
1.9562
1.0027
2.2073
-1.5778
1.5977
2.003
3.2606











P9
LDGT1
86-89
0.25683
0.17484
0.544
2.0132
0.9204
2.1628
-2.2132
0.7045
2.0543
2.3838
3.4294








V

P10
LDGT1
86-89
0.25153
0.17214
0.4457
2.0253
0.8623
1.9864
-2.2523
0.4119
2.2232
1.6726
1.6078
3.6006









P11
LDGT1
86-89
0.19659
0.07478
2.1606
0.6298
1.0463
1.2006
-1.6683
1.7941
1.4349
0.7533
1.4174
2.3957
1.957








P12
LDGT1
86-89
0.17271
0.05609
2.4645
0.4889
0.5378
1.0905
-0.6636
1.2017
1.3698
1.1634
0.8772
3.332
1.2131
1.5195







P13
LDGT1
86-89
0.13816
0.05847
2.1195
0.4529
0.7286
1.2692
-0.7285
1.427
1.284
0.6442
0.4274
2.4657
0.9117
0.2322
2.6432






P14
LDGT1
86-89
0.12661
0.04856
2.2389
0.3811
0.6514
1.4033
-0.4687
1.3427
1.1456
0.9582
0.0864
2.2192
0.7164
0.4248
1.479
2.6071





P15
LDGT1
86-69
0.11362
0.03727
2.1223
0.3411
0.6537
1.4601
-0.202
1.1529
1.232
0.6887
-0.2172
2.1843
0.7658
0.6133
0.9282
0.7457
2.1404




P16
LDGT1
86-89
0.10769
0.03525
1.9852
0.3076
0.7867
1.4374
-0.022
1.1859
1.1329
0.515
-0.1541
2.0989
0.7572
0.7681
0.784
0.499
0.7808
4.7323



P17
LDGT1
86-89
0.05356
0.01318
1.0985
0.6236
0.7621
0.7653
0.5658
1.0327
0.7036
0.4129
0.7975
1.2149
0.7601
0.6019
0.8454
0.937
0.7894
0.9132
3.009


P18
LDGT1
86-89
0.04127
0.00269
1.2483
0.5985
0.678
0.9508
0.3871
1.095
0.7732
0.6052
0.8039
0.761
0.6892
0.7308
0.7205
0.82
0.9126
0.0726
2.0163
1.4844

P19
LDGT1
86-89
0.03395
0.00397
1.0933
0.6168
0.695
0.9028
0.4507
1.0319
0.9511
0.5369
0.7983
0.6339
0.6758
0.6909
0.7183
0.7503
0.7572
-0.3355
1.6677
1.2005
0.8458
IM147 Phase 2 Regression Coefficients, HC, 1986 to 1989 Model Year LDGTI's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT1
86-89
0.29834
0.1158









i
3.3457








B12
LDGT1
86-89
0.26979
0.09053










2.3987
1.9564







B13
LDGT1
86-69
0.20617
0.08073










1.4658
0.0492
4.1671






B14
LDGT1
86-89
0.19052
0.06515










1.1799
0.3012
2.4794
3.6519





B15
LDGT1
86-69
0.16882
0.04542










1.1829
0.6531
1.4949
0.6859
3.2881




B16
LDGT1
86-89
0.16083
0.04255










1.1483
0.9014
1.2632
0.3783
1.3378
6.662



B17
LDGT1
86-89
0.07626
0.01566










1.0548
0.7801
1.1751
1.2709
1.2392
0.9683
4.2486


B18
LDGT1
86-89
0.05999
0.00012










0.9466
0.9733
1.0114
1.038
1.3674
-0.1144
2.9584
1.987

B19
LDGT1
86-69
0.04869
0.00402










0.9313
0.9308
0.9901
0.9768
1.1033
-0.6703
2.4005
1.5697
1.23

-------
1
M147 Composite Regression Coefficients, HC, 1990 to 1995 Model Year LDGH's





Repression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT1
90-95
0.37406
0.22818
17.1854


















P2
LDGT1
90-95
0.23507
0.05902
-2.145
5.1262

















P3
LDGT1
90-95
0.21189
0.05784
-1.1729
3.3619
3.4597
















P4
LDGT1
90-95
0.20276
0.05212
-2.7245
3.2374
1.6019
3.7987















P5
LDGT1
90-95
0.19505
0.05057
-3.0392
2.7597
1.1351
2.24
3.8993














P6
LDGT1
90-95
0.18478
0.05162
-3.3501
2.6388
0.6397
1.4629
1.9249
4.9984













P7
LDGT1
90-95
0.17282
0.04206
-2.9083
2.3805
0.6434
1.9124
-0.3621
3.0178
4.1577












P8
LDGT1
90-95
0.16217
0.03882
-1.4065
1.7566
1.0327
1.3221
-0.6046
1.7837
2.9574
4.5516











P9
LDGT1
90-95
0.15997
0.03954
-1.5284
1.8223
0.7356
1.6157
-0.8447
0.9682
2.5193
3.7565
3.0158










P10
LDGT1
90-95
0.15899
0.03962
-1.3222
1.7605
0.8841
1.526
-0.7219
0.6441
2.5385
3.3964
1.3921
2.2962









P11
LDGT1
90-95
0.1242
0.00888
0.4575
0.8942
1.2216
1.078
-0.2219
0.7626
1.9585
1.2513
1.7074
1.5915
1.5461








P12
LDGT1
90-95
0.108
0.00853
0.5588
0.7543
0.7122
1.489
-0.1897
0.6244
1.9355
0.7067
0.8076
2.4795
0.9978
1.8755







P13
LDGT1
90-95
0.08846
0.01744
0.8836
0.5089
0.8095
1.7266
0.0115
0.5537
1.4386
0.4008
0.5776
1.904
0.7549
0.8219
2.3703






P14
LDGT1
90-95
0.07835
0.01772
0.858
0.474
0.6951
1.5244
0.3476
0.7542
1.4394
0.4383
0.7477
2.2305
0.5943
0.9555
0.9132
2.1616





P15
LDGT1
90-95
0.06853
0.01326
0.7115
0.5394
0.7616
1.4283
0.4159
0.4769
1.3363
0.4071
0.6623
1.647
0.6826
0.8356
0.5762
0.7447
2.2358




P16
LDGT1
90-95
0.0593
0.01319
0.6956
0.6235
0.517
1.2912
0.6027
0.5332
1.4561
0.3365
0.4783
1.6692
0.6904
0.8659
0.7576
0.7049
0.1973
5.5813



P17
LDGT1
90-95
0.04054
0.0066
0.721
0.7143
0.508
0.9341
0.7735
0.3397
1.3289
0.8072
0.4656
0.9685
0.6893
0.7801
0.6168
0.8507
0.941
2.2139
2.3062


P18
LDGT1
90-95
0.03216
0.00497
0.6809
0.6934
0.5834
0.9437
0.759
0.6708
0.9442
0.9559
0.356
0.8599
0.6671
0.8103
0.5884
1.0269
0.3629
1.3443
1.5874
1.5359

P19
LDGT1
90-95
0.02955
0.00468
0.7103
0.6964
0.6123
0.9353
0.834
0.5442
0.9229
0.8944
0.4112
0.8891
0.6898
0.7038
0.6909
0.7838
0.3123
0.6669
1.5046
1.3036
0.7468
IM147 Phase 2 Regression Coefficients, HC, 1990 to 1995 Model Year LDGH's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT1
90-95
0.19826
0.0335










3.1539








B12
LDGT1
90-95
0.16235
0.0226










1.7885
3.1368







B13
LDGT1
90-95
0.12683
0.02868










1.1305
1.1678
3.5627






B14
LDGT1
90-95
0.11708
0.02986










0.9821
1.3215
2.042
2.4823





815
LDGT1
90-95
0.09769
0.0205










1.024
1.124
1.0503
0.5065
3.5529




B16
LDGT1
90-95
0.08453
0.02072










1.0421
1.0995
1.3341
0.4695
0.6556
7.7639



B17
LDGT1
90-95
0.05719
0.01154










1.0027
0.9977
1.0045
0.9826
1.4994
2.7725
3.1834


B18
LDGT1
90-95
0.04461
0.008










0.9558
1.0711
0.8811
1.2953
0.5862
1.7403
2.1672
2.144

B19
LDGT1
90-95
0.04108
0.00785










0.9823
0.9356
1.0185
0.9731
0.499
0.8303
2.0505
1.8394
1.0152

-------
IM147 Composite Regression Coefficients, HC, 1996 and Newer Model Year LDGH's
Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT1
96+
0.05316
0.03402
18.1995


















P2
LDGT1
96+
0.04307
0.02411
0.2121
2.417

















P3
LDGT1
96+
0.03308
0.02193
2.6279
1.1433
2.9158
















P4
LDGT1
96+
0.0321
0.02215
3.4627
1.1835
1.5689
1.9266















P5
LDGT1
96+
0.0267
0.01928
1.088
0.5294
2.0247
-3.8687
8.4714














P6
LDGT1
96+
0.02641
0.01933
0.5547
0.4887
1.8979
-4.0225
7.8031
1.8996













P7
LDGT1
96+
0.02534
0.01654
0.953
0.3268
2.2496
-3.2672
4.7342
1.2515
3.3211












P8
LDGT1
96+
0.02524
0.01597
-1.5963
0.6417
2.0178
-4.3447
3.8465
1.9882
2.8209
1.9699











P9
LDGT1
96+
0.02522
0.0165
-0.9944
0.5289
2.1342
-4.3968
4.6568
1.7412
2.8435
2.4683
-2.6396










P10
LDGT1
96+
0.02529
0.01642
-1.4673
0.5347
2.3503
-5.1056
4.7009
2.0105
2.7607
2.6563
-1.6108
-0.7547









P11
LDGT1
96+
0.01755
0.00739
3.4599
01081
1.652
-1.1947
3.4359
0.4622
0.6672
0.794
-0.5304
-0.2246
1.7144








P12
LDGT1
96+
0.0151
0.00659
3.8459
0.4104
0.2567
1.1211
0.4848
0.8471
1.2853
0.5367
1.1122
0.0179
1.1163
1.9287







P13
LDGT1
96+
0.01241
0.00389
1.7514
0.4378
1.7539
-0.5187
-0.8979
0.5987
2.4378
1.2817
1.9041
-1.2482
0.9844
0.1784
2.6171






P14
LDGT1
96+
0.01176
0.00367
1.875
0.4721
1.4005
-0.0285
-0.4781
0.4911
2.4771
0.9339
1.5111
-0.5207
0.7769
0.1877
2.2359
1.1134





P15
LDGT1
96+
0.00769
0.00241
1.0482
0.6074
0.9552
-0.3978
1.4364
1.1394
0,7747
1.4416
0.0533
-0.1206
0.7233
0.4499
1.3519
0.2003
2.2561




P16
LDGT1
96+
0.00774
0.0024
1.0165
0.6108
0.9582
-0.4078
1.4359
1.1436
0.7653
1.4501
0.0568
-0.1213
0.7218
0.4484
1.3528
0.1992
2.2428
0.0735



P17
LDGT1
96+
0.00585
0.00306
1.3687
0.5499
1.0977
0.7636
0.4488
0.6346
0.7879
0.6314
0.3617
0.6995
0.7613
0.3632
1.0963
0.709
0.7912
1.2412
3.4916


P18
LDGT1
96+
0.00432
0.00121
1.569
0.5806
0.9368
0.6842
0.9482
0.5032
0.7086
0.6239
0.5212
0.4646
0.6817
0.6338
0.8277
0.6983
0.744
0.4028
2.2628
1.7838

P19
LDGT1
96+
0.00416
0.00114
1.2715
0.6218
0.8437
1.0272
0.6835
0.4345
0.8099
0.3982
0.7078
0.75
0.6649
0.7239
0.6761
0.8112
0.5347
0.2273
2.2233
1.3046
0.5973

IM147
Phase 2 Regression Coefficients, HC, 1996 and Newer Model Year LDGTVs





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT1
96+
0.02393
0.00865










2.4226








B12
LDGT1
96+
0.02095
0.00863










1.6528
1.9763







B13
LDGT1
96+
0.01824
0.00691










1.444
0.667
2.5512






B14
LDGT1
96+
0.01653
0.00607










1.2007
0.3963
2.3614
1.9099





B15
LDGT1
96+
0.01077
0.00256










1.0854
0.6091
1.8127
0.4665
3.018




B16
LDGT1
96+
0.01076
0.00241










1.0549
0.6405
1.7899
0.4709
2.8141
0.9908



B17
LDGT1
96+
0.00794
0.00414










0.8756
0.7439
1.2805
1.0073
1.458
1.4709
3.9228


B18
LDGT1
96+
0.00581
0.00161










0.8235
0.9534
1.0124
1.0609
1.0031
0.8433
3.018
2.4016

B19
LDGT1
96+
0.00549
0.00161










0.823
1.0394
0.8747
1.0551
0.9342
0.2958
2.5227
1.8686
0.7675

-------
1M147 Composite Regression Coefficients, HC, 1981 to 1985 Model Year LDGT2's
Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT2
81-85
0.92959
1.08952
14.1316


















P2
LDGT2
81-85
0.49433
0.24032
2.1062
6.4379

















P3
LDGT2
81-85
0.47471
0.22581
0.921
5.3132
2.7842
















P4
LDGT2
81-85
0.43631
0.17782
-1.7872
5.4763
0.3261
3.8518















P5
LDGT2
81-85
0.42908
0.2114
-1.5166
4.7372
0.0105
2.9709
2.5245














P6
LDGT2
81-85
0.41687
0.23473
-3.8569
4.5338
-0.9009
2.7375
1.9498
3.2209













P7
LDGT2
81-85
0.41066
0.23366
-3.2863
4.2499
-1.1231
2.3552
1.2812
2.4743
2.1991












P8
LDGT2
81-85
0.3672
0.16688
-1.6179
3.5835
-0.8816
1.6341
-0.5481
3.1967
0.3748
5.7221











P9
LDGT2
81-85
0.3566
0.18117
-1.9127
3.5055
-0.9418
2.6495
-0.9804
1.9891
0.0338
4.0901
3.7721










P10
LDGT2
81-85
0.34151
0.16655
-1.8705
3.3075
-0.9637
3.3159
-1.7891
1.43
0.0942
4.2235
1.3603
3.899









P11
LDGT2
81-85
0.2931
-0.04548
1.0746
1.8029
-1.2122
2.864
-0.6499
0.7309
0.2679
3.2607
-0.113
4.0698
1.9325








P12
LDGT2
81-85
0.2758
-0.01823
0.7863
1.8538
-0.984
2.6077
-0.7763
0.9299
0.773
2.1956
-0.3985
4.4575
0.873
2.4503







P13
LDGT2
81-85
0.21405
-0.01142
0.4126
1.5075
-0.6583
2.6141
-0.5064
0.917
1.449
-0.2757
1.0903
2.7846
0.8509
0.03
3.3522






P14
LDGT2
81-85
0.17574
-0.01255
0.2502
1.2847
-0.0742
1.733
0.4325
1.3417
1.2187
0.2611
1.1197
1.7719
0.3758
0.7006
0.8312
4.2084





P15
LDGT2
81-85
0.15062
-0.02221
0.8141
1.325
-0.0587
1.4355
0.2426
1.2996
1.3459
0.694
0.5531
1.836
0.507
0.8254
-0.1245
2.1165
2.3277




P16
LDGT2
81-85
0.14738
-0.02755
1.0121
1.3194
-0.0133
1.4206
0.2735
1.0742
1.3679
0.895
0.1188
2.0783
0.5591
0.9525
-0.3444
1.9718
1.5317
2.7401



P17
LDGT2
81-85
0.11644
-0.04735
1.4605
1.1844
0.1561
0.8181
0.2509
0.6122
1.2664
1.4097
-0.3806
2.1461
0.6223
0.997
-0.2513
1.5706
1.3565
1.6797
2.2361


P18
LDGT2
81-85
0.10411
-0.05875
1.9421
1.2153
0.2284
0.6248
0.1763
0.9731
0.9762
1.0664
-0.3199
1.6679
0.7965
0.7901
0.01
1.0203
0.9553
1.6582
1.5344
1.4185

P19
LDGT2
81-85
0.08606
-0.02861
1.9188
1.1185
0.3074
0.5037
0.6515
0.9559
0.5634
1.1722
0.4972
1.0016
0.7179
0.584
0.4013
1.317
0.5551
-0.9186
1.2833
0.9697
1.3485

IM147 Phase 2 Regression Coefficients, HC, 1981 to 1985 Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT2
81-85
0.53604
-0.06735










4.7635








B12
LDGT2
81-85
0.50867
-0.03629










2.9814
3.9143







B13
LDGT2
81-85
0.37516
-0.03573










2.2833
-1.1494
5.7782






B14
LDGT2
81-85
0.31715
-0.05881










1.5613
0.0866
1.7691
6.5887





B15
LDGT2
81-85
0.29115
-0.05873










1.6798
0.2156
0.484
3.6579
3.2814




B16
LDGT2
81-85
0.28504
-0.05551










1.6963
0.4373
0.1942
3.4658
1.7593
4.9957



B17
LDGT2
81-85
0.19206
-0.07062










1.2418
0.7375
0.4405
2.7184
1.6887
1.1505
4.1633


B18
LDGT2
81-85
0.17741
-0.07525










1.3222
0.5566
0.4852
2.1651
0.9953
1.6325
3.2854
1.6783

B19
LDGT2
81-85
0.15121
-0.03936










1.1587
0.4814
1.1411
2.1958
0.3955
-1.7449
2.7898
1.0076
1.9981

-------
IM147 Composite Regression Coefficients, HC, 1986 to 1987 Model Year LDGT2's
Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT2
86-87
0.76767
0.79061
14.82


















P2
LDGT2
86-87
0.53753
0.28237
1.2627
6.2363

















P3
LDGT2
86-87
0.38485
0.18393
-0.7945
3.7354
5.7608
















P4
LDGT2
86-87
0.34875
0.16586
-2.6149
3.7629
2.8334
4.1762















P5
LDGT2
86-87
0.34617
0.17953
-3.6873
3.212
2.761
3.4285
2.4018














P6
LDGT2
86-87
0.29325
0.20542
-3.5626
3.7025
1.7413
0.4872
1.0679
4.3236













P7
LDGT2
86-87
0.2934
0.19684
-3.5264
3.6876
1.7296
0.6143
0.2317
4.2624
0.8683












P8
LDGT2
86-87
0.28213
0.17396
-1.8292
3.3926
1.1562
0.6702
0.0208
3.449
0.3557
2.8062











P9
LDGT2
86-87
0.27183
0.1983
-1.1117
3.3764
1.0296
0.699
0.2722
2.4556
0.8353
0.6
2.1369










P10
LDGT2
86-87
0.26655
0.20643
-0.6291
3.3422
0.5757
0.6074
0.6213
2.1855
0.9202
0.0129
1.3566
1.7368









P11
LDGT2
86-87
0.23982
0.0819
0.4024
2.3049
0.6701
0.0523
1.2179
2.4136
0.3361
0.0779
0.9104
1.6901
1.4014








P12
LDGT2
86-87
0.21585
0.04628
0.8765
1.6118
0.7539
0.3407
0.9193
1.8565
0.8585
0.4701
0.4832
1.6797
1.0229
2.1489







P13
LDGT2
86-87
0.17953
0.04669
1.6471
0.9949
0.685
1.0029
0.4324
1.1786
0.7455
0.9956
-0.3607
1.9373
1.0646
0.024
2.6969






P14
LDGT2
86-87
0.16923
0.01715
2.1778
0.6591
1.0222
0.7528
0.0545
1.7038
0.8256
0.5799
0.1446
1.6426
1.1863
0.1661
1.154
2.4636





P15
LDGT2
86-87
0.16212
0.02841
2.045
0.4649
0.7435
1.0759
0.3389
1.3816
1.0395
0.9294
0.0477
1.5028
1.0456
0.1781
0.9342
1.5234
1.2274




P16
LDGT2
86-87
0.15721
0.03244
2.1199
0.4169
0.672
0.9335
0.3563
1.582
1.3979
0.0804
0.2926
1.6162
1.1907
0.4448
0.6092
0.8887
0.3523
3.6537



P17
LDGT2
86-87
0.08118
-0.01059
0.6472
0.7516
0.3931
0.9394
1.5188
0.882
0.3132
0.5162
0.8422
0.3101
0.8207
0.6645
0.7572
1.2216
0.5723
1.2293
3.38


P18
LDGT2
86-87
0.06878
-0.02245
0.747
0.8866
0.3561
0.7928
1.5497
0.9638
-0.0777
0.7405
0.8591
0.4782
0.7233
0.7721
0.8208
0.8129
0.6412
0.1007
2.9199
1.2143

P19
LDGT2
86-87
0.05744
-0.02534
0.8277
0.9324
0.4772
0.6834
1.4838
0.7072
0.6021
0.5295
0.8642
0.1132
0.7642
0.7717
0.5785
0.3706
0.4851
0.0561
2.1533
0.9384
1.4322


IM147 Phase 2 Regression Coefficients, HC, 1986 to 1987 Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT2
86-87
0.42555
0.04924










4.1247








B12
LDGT2
86-87
0.34262
0.01535










2.3444
4.5554







B13
LDGT2
86-87
0.26401
0.06727










1.8518
0.0098
4.3001






B14
LDGT2
86-87
0.26019
0.04305










1.8614
0.0618
3.3888
1.611





B15
LDGT2
86-87
0.24666
0.06334










1.6413
0.0316
2.594
0.5597
1.9278




B16
LDGT2
86-87
0.24422
0.06216










1.731
0.2275
2.4543
-0.1869
1.1753
3.2006



B17
LDGT2
86-87
0.11773
-0.01838










1.1424
0.827
1.1111
1.9999
0.6793
1.223
4.3846


B18
LDGT2
86-87
0.10087
-0.03741










1.0229
1.0633
1.2587
1.1759
0.8269
0.1785
3.8251
1.6179

B19
LDGT2
86-87
0.09638
-0.03963










1.0707
1.0296
1.0107
1.2016
0.7069
-0.201
3.2092
1.6709
0.8058

-------
1
M147 Composite Regression Coefficients, HC, 1988 to 1995 Model Year LDGT2's





Repression Coefficients
Segment,
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT2
88-95
0.48034
0.5235
11.3071


















P2
LDGT2
88-95
0.33713
0.21906
-2.96
4.9422

















P3
LDGT2
88-95
0.29651
0.19834
-1.7489
2.8353
4.9489
















P4
LDGT2
88-95
0.28747
0.18089
-1.9477
2.7951
2.9733
3.2647















P5
LDGT2
88-95
0.27016
0.17705
-2.2098
1.8312
2.2325
1.406
5.8661














P6
LDGT2
88-95
0.25421
0.19481
-2.5612
2.2441
0.5267
0.4177
3.0039
5.8248













P7
LDGT2
88-95
0.24477
0.17308
-2.1598
1.867
0.6968
0.5785
0.8635
4.352
4.2662












P8
LDGT2
88-95
0.22604
0.14292
-1.1587
1.5845
0.5456
0.1762
0.5776
1.9469
3.7472
4.4401











P9
LDGT2
88-95
0.22226
0.14551
-1.2733
1.703
0.284
0.1479
0.9179
0.6334
3.6501
3.448
2.3522










P10
LDGT2
88-95
0.21442
0.13617
-0.7091
1.5866
0.3214
0.3077
0.4365
-0.1063
3.9046
2.8806
-0.9464
5.4516









P11
LDGT2
88-95
0.1654
0.04125
0.6538
0.773
0.5132
0.6898
0.6807
0.4255
2.0621
1.1813
0.4048
2.5588
2.1807








P12
LDGT2
88-95
0.14608
0.03783
1.0502
0.5966
0.307
0.8399
0.3903
0.6138
1.7604
1.3774
-0.14
2.8081
1.4104
2.3284





f

P13
LDGT2
88-95
0.08638
0.03352
1.3017
0.5004
0.8055
0.8896
0.2765
1.4656
0.5695
0.5182
0.3106
1.2433
0.9247
-0.2803
4.1519






P14
LDGT2
88-95
0.07556
0.02491
1.2979
0.524
0.7653
0.8982
0.3475
1.5181
0.5104
0.7567
0.2981
1.4094
0.6811
0.0851
2.7444
1.9985





P15
LDGT2
88-95
0.06718
0.02356
1.1938
0.5183
0.8188
0.8118
0.379
1.4304
0.7007
0.8512
0.4013
1.2797
0.6663
0.2996
1.6768
0.8504
1.7815




P16
LDGT2
88-95
0.06237
0.02258
1.1224
0.5458
0.8938
0.7295
0.4333
1.4798
0.6019
0.7965
0.4454
1.2725
0.6721
0.4389
1.2102
0.7846
1.1152
3.1105



P17
LDGT2
88-95
0.04421
0.01056
0.9076
0.7137
0.6003
0.6133
0.8089
0.9512
0.7459
0.7414
0.5887
0.9827
0.6061
0.6414
0.972
0.8458
1.1687
1.7368
1.9774


P18
LDGT2
88-95
0.03718
0.00462
0.9099
0.6818
0.6872
0.6822
0.7561
0.9525
0.6907
0.7852
0.6052
1.0177
0.6169
0.7309
0.6146
0.7739
1.0312
0.9695
1.4043
1.372

P19
LDGT2
88-95
0.03217
0.00297
0.8825
0.7085
0.7059
0.6951
0.7723
0.8271
0.7424
0.7667
0.703
0.8462
0.6346
0.6886
0.6361
0.6291
0.7448
0.7476
1.2353
1.0716
0.9076
IM147 Phase 2 Regression Coefficients, HC, 1988 to 1995 Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT2
88-95
0.23751
0.05786










3.8759








B12
LDGT2
88-95
0.2067
0.04911










2.4274
3.3621







B13
LDGT2
88-95
0.1199
0.03897










1.2412
-0.4078
5.5892






B14
LDGT2
88-95
0.10757
0,02785










1.0283
0.0365
3.9664
2.4645





B15
LDGT2
88-95
0.09668
0.02657










1.0392
0.3282
2.5887
0.8805
2.4135




B16
LDGT2
88-95
0.09084
0.02471










1.0542
0.5363
1.9743
0.7835
1.5325
4.1018



B17
LDGT2
88-95
0.06106
0.01248










0.8883
0.8408
1.4659
1.0499
1.5851
2.3095
2.7427


B18
LDGT2
88-95
0.05212
0.00425










0.9142
0.9654
1.0256
0.9252
1.4081
1.2094
2.0957
1.7973

B19
LDGT2
88-95
0.0449
0.00374










0.9299
0.9243
1.0294
0.748
0.9948
0.9323
1.7813
1.4026
1.2714

-------
IM
147 Composite Repression Coefficients, HC, 1996 and Newer Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT2
96+
0.07243
0.06002
10.2


















P2
LDGT2
96+
0.04564
0.0237
-7.2812
4.6718

















P3
LDGT2
96+
0.04313
0.03223
-2.4032
1.3094
6.1404
















P4
LDGT2
96+
0.03729
0.03292
-1.4437
2.1679
-2.2327
9.7739















P5
LDGT2
96+
0.02887
0.03311
-7.1815
1.5592
3.8084
-6.997
8.5612














P6
LDGT2
96+
0.02468
0.03246
-5.2125
2.8958
-0.7992
-3.162
2.3757
7.8985













P7
LDGT2
96+
0.02471
0.03212
-5.6903
2.6269
-0.3486
-3.879
1.8571
6.769
2.1598












P8
LDGT2
96+
0.02458
0.03221
-5.7203
2.2642
1.1436
-7.0745
3.5654
5.3631
0.5718
2.5776











P9
LDGT2
96+
0.02153
0.02755
-5.6174
3.2239
-0.1186
-6.6505
2.7679
1.8663
-1.0747
0.5327
8.5733










P10
LDGT2
96+
0.02175
0.0276
-5.5557
3.1793
-0.113
-6.663
2.9877
1.5294
-1.4181
0.9223
7.6307
0.9647









P11
LDGT2
96+
0.01561
0.01512
-0.1593
1.3225
0.8979
-3.5644
1.4176
3.2321
-2.661
2.4201
3.7658
1.2056
1.069








P12
LDGT2
96+
0.01535
0.01155
0.1184
1.3108
0.2317
-1.9686
1.0103
3.2791
-1.9369
2.0526
2.5266
1.346
0.9439
0.9442







P13
LDGT2
96+
0.0118
0.00591
0.5316
0.8014
1.3453
-1.5835
1.0716
4.5702
-0.6276
1.6068
1.2052
-0.739
0.9742
-0.8137
3.1462






P14
LDGT2
96+
0.00894
0.00333
0.9294
0.6687
0.8295
0.2036
0.5645
4.0094
0.2628
1.1846
-0.1226
0.2024
0.7931
-0.6864
2.3879
2.3089





P15
LDGT2
96+
0.00519
0.00298
0.5728
0.3247
2.2863
-2.5268
2.2831
1.4541
-0.658
2.6037
-0.4889
1.5224
0.8251
-0.0309
1.4354
0.4248
1.7293




P16
LDGT2
96+
0.00341
0.00091
0.6938
0.6433
0.9494
-0.2435
1.2108
1.9848
0.1134
1.3832
0.4148
0.7129
0.7604
0.2037
1.1446
0.8148
0.7145
4.0429



P17
LDGT2
96+
0.003
0.0006
0.5483
0.8035
0.6446
0.0208
1.0382
2.0883
0.1055
1.0991
0.7076
0.6796
0.7787
0.2756
1.0048
0.7528
0.7994
3.2034
1.1992


P18
LDGT2
96+
0.00229
0.00034
0.8423
0.7701
0.5405
0.6178
0.7621
1.54
0.2461
0.9463
0.785
0.7135
0.7393
0.5553
0.7347
0.5376
0.9234
2.5586
0.3528
1.0986

P19
LDGT2
96+
0.00148
-0.00015
0.5489
0.7737
0.6253
0.5369
0.7319
1.0865
0.7396
0.777
0.8586
0.6472
0.714
0.5043
0.8109
0.8951
0.552
1.6026
0.688
0.6722
0.8733
IM147
Phase 2 Regression Coefficients, HC, 1996 and Newer Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT2
96+
0.02667
0.01375










1.9495








B12
LDGT2
96+
0.02266
0.00456










1.3011
3.138







B13
LDGT2
96+
0.01837
-0.00086










1.3623
0.2518
4.0937






B14
LDGT2
96+
0.015
-0.00298










1.1501
0.0589
2.9165
3.3772





B15
LDGT2
96+
0.01018
-0.00336










1.1443
0.9415
1.7337
0.5943
2.5841




B16
LDGT2
96+
0.00753
-0.00463










1.0828
1.0417
1.2129
0.903
1.0508
6.8124



B17
LDGT2
96+
0.00686
-0.00476










1.1331
1.0977
1.0456
0.7683
1.3074
4.7654
2.3941


B18
LDGT2
96+
0.00371
-0.00109










0.9732
1.1893
0.7137
0.4116
1.4972
2.8064
-0.0005
2.4412

B19
LDGT2
96+
0.00243
-0.00121










0.9785
0.9384
0.9374
1.0534
0.8374
1.4572
0.714
1.4339
1.3705

-------



M147 Composite Regression Coefficients, HC, 1981 to 1982 Model Year LDGV's





Regression Coefficients
Segment
Number
;

RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
81-82
0.49218
0.57705
15.1354


















P2
LDGV
81-82
0.33532
0.27587
2.7958
4.3107

















P3
LDGV
81-82
0.28385
0.2387
3.237
1.8573
5.3824
















P4
LDGV
81-82
0.26651
0.22287
1.6681
2.0142
3.3342
3.0522















P5
LDGV
81-82
0.25039
0.21722
-0.3542
1.7343
3.0584
-0.4158
5.9309














P6
LDGV
81-82
0.23492
0.22612
-1.1998
1.7931
2.0663
-0.0624
2.4987
4.8232













P7
LDGV
81-82
0.22127
0.21033
-1.4452
1.5589
1.4953
0.1839
1.2039
3.8754
3.5496












P8
LDGV
81-82
0.21552
0.19445
' -0.9213
1.4891
1.3035
-0.0672
1.0624
2.967
2.9559
2.5679











P9
LDGV
81-82
0.2067
0.18041
-1.0513
1.5796
1.3012
-0.0519
1.564
-0.0917
1.2979
2.0321
6.4454










P10
LDGV
81-82
0.18913
0.16356
-1.3284
1.5978
1.0833
0.3527
0.1824
0.3708
1.1991
2.1788
0.884
7.9922









P11
LDGV
81-82
0.15452
0.0563
0.0087
0.5393
1.7119
0.1012
0.6423
0.9748
1.0961
0.177
1.9283
6.7153
1.4557








P12
LDGV
81-82
0.13848
0.03382
0.3059
0.4616
1.2496
0.2523
0.8742
0.2381
0.6512
1.0074
1.8257
7.1
0.8496
1.7116







P13
LDGV
81-82
0.11063
0.03458
1.1018
0.2871
1.8035
0.9765
-0.5314
0.4907
1.1534
0.6794
1.3477
4.1068
0.6837
0.0626
3.0307






P14
LDGV
81-82
0.10379
0.02583
0.937
0.3096
1.715
1.0249
-0.5161
0:4784
1.2933
0.6542
1.7892
3.4911
0.4746
0.3833
2.0844
1.8075





P15
LDGV
81-82
0.09549
0.02733
1.3271
0.2571
1.7659
0.7304
0.2147
0.1823
1.1956
0.9924
1.0719
2.8131
0.4955
0.6194
1.2124
0.4439
2.0988




P16
LDGV
81-82
0.07932
0.0312
1.0118
0.4766
1.3033
0.8794
0.0472
0.402
1.6192
1.449
-0.2368
2.1792
0.4806
0.6063
1.0279
0.7352
0.5135
5.3144



P17
LDGV
81-82
0.05521
0.00814
1.2935
0.6849
0.6167
0.5737
0.616
0.8743
0.9448
1.724
0.1427
0.6903
0.6243
0.6678
0.6972
1.4033
0.6874
2.3373
2.2858


P18
LDGV
81-82
0.04359
0.00235
1.4765
0.7063
0.5683
0.9993
-0.1434
1.1276
0.8748
1.3533
-0.3586
1.3373
0.6347
0.6998
0.796
0.716
0.6921
1.817
1.4148
1.4078

P19
LDGV
81-82
0.03591
0.00148
1.2991
0.7134
0.6447
0.8407
0.2792
0.9872
0.8646
0.9518
0.1384
1.1208
0.6929
0.6275
0.8021
0.5739
0.7875
-0.2237
1.2967
0.9834
1.1392
IM14
\1 Phase 2 Regression Coefficients, HC, 1981 to 1982 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
81-82
0.32813
0.13912










3.2894








B12
LDGV
81-82
0.29163
0.08753










1.8437
3.4587







B13
LDGV
81-82
0.18796
0.06684










0.9136
-0.314
6.0599






B14
LDGV
81-82
0.1799
0.05326










0.6172
0.1991
4.6274
2.6027





B15
LDGV
81-82
0.15358
0.05277










0.6903
0.7618
2.1672
-0.1274
4.2479




B16
LDGV
81-82
0.12174
0.04997










0.8196
0.7862
1.6353
0.667
1.0053
8.5142



B17
LDGV
81-82
0.08162
0.00913










1.0095
0.7175
1.2863
1.8724
0.9087
3.3981
3.148


B18
LDGV
81-82
0.06421
0.00073










0.9515
0.8623
1.1518
0.998
0.9777
2.5859
1.9343
1.9181

B19
LDGV
81-82
0.05142
0.00017










0.9854
0.8032
1.1865
0.7374
1.1
-0.3037
1.7543
1.3113
1.6987

-------

M147 Composite Regression Coefficients, HC, 1983 to 1985 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
83-85
0.40712
0.48122
11.2061


















P2
LDGV
83-85
0.28584
0.23584
0.3793
4.0312

















P3
LDGV
83-85
0.2251
0.18194
0.1504
1.6625
5.8839
















P4
LDGV
83-85
0.20949
0.1636
-0.6566
1.89
3.4063
3.0941















P5
LDGV
83-85
0.19447
0.16094
-1.0863
1.2885
2.6869
2.1291
3.7766














P6
LDGV
83-85
0.19153
0.16531
-1.4624
1.3044
2.1907
1.8106
3.1299
2.0652













P7
LDGV
83-85
0.18355
0.1542
-1.1191
1.2382
2.1956
1.3759
2.2879
1.3053
2.1756


N









P8
LDGV
83-85
0.1742
0.14505
-1.1029
1.1144
1.7862
0.8889
2.4267
1.2368
1.6895
1.9949











P9
LDGV
83-85
0.16499
0.14815
-0.7229
1.073
1.8176
0.7769
1.4795
0.6194
1.4189
0.9084
4.175










P10
LDGV
83-85
0.16024
0.14347
-0.7243
1.1373
1.5682
0.8427
0.8657
0.8685
1.1993
0.834
2.645
2.9155









P11
LDGV
83-85
0.12238
0.06297
0.38
0.2293
1.7054
0.1938
1.2879
1.6893
0.5856
0.5369
2.6104
1.7875
1.5441








P12
LDGV
83-85
0.10785
0.05022
0.4878
0.4094
0.8846
0.5066
1.0774
1.8031
0.7369
0.5491
1.8603
2.2819
1.0196
1.6327







P13
LDGV
83-85
0.07961
0.0331
0.7767
0.4332
0.8179
0.9806
1.0015
1.2108
0.561
0.4915
1.5593
1.6272
0.8246
0.3997
2.5228






P14
LDGV
83-85
0.068
0.02445
0.861
0.3782
0.9405
0.9692
0.9471
1.0556
0.6664
0.4702
1.5692
1.559
0.6216
0.5139
1.483
2.5758





P15
LDGV
83-85
0.05617
0.01633
0.6641
0.3847
0.9494
0.9339
0.8878
1.1718
0.6879
0.3691
1.2553
1.3016
0.7499
0.5579
0.6795
1.1332
2.2871




P16
LDGV
83-85
0.05545
0.01675
0,7233
0.393
0.9564
0.9028
0.9463
1.1024
0.7194
0.3887
1.2701
1.1977
0.7436
0.5865
0.627
1.1212
1.8334
1.417



P17
LDGV
83-85
0.03879
0.00841
0.893
0.5126
0.7936
0.6136
1.0495
0.8218
0.8047
0.6079
0.984
0.9734
0.7209
0.7276
0.5005
1.2506
1.4269
0.4297
2.417


P18
LDGV
83-85
0.02939
-0.0015
0.9063
0.6276
0.7336
0.799
0.8401
0.7909
0.6547
0.6256
1.0592
0.5782
0.7013
0.749
0.5944
0.8615
1.0441
0.6308
1.7952
1.3705

P19
LDGV
83-85
0.0284
-0.00036
0.8248
0.6354
0.7403
0.7849
0.8885
0.8016
0.6772
0.6005
1.0662
0.4753
0.7239
0.7376
0.6183
0.7249
0.9182
0.3388
1.7224
1.1415
0.5047
IM14
Y7 Phase 2 Regression Coefficients, HC, 1983 to 1985 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
1
C10 1
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
83-85
0.2057
0.10209










2.848








B12
LDGV
83-85
0.17708
0.0755










1.8316
2.7713







B13
LDGV
83-85
0.12398
0.04472









L
1.2782
0.5298
4.0757






B14
LDGV
83-85
0.10965
0.03254










0.9666
0.713
2.5673
3.6248





B15
LDGV
83-85
0.08809
0.01872









.
1.0596
0.7937
1.0694
1.2338
3.6814




B16
LDGV
83-85
0.08613
0.02003










1.0528
0.8555
0.9553
1.2083
2.7852
2.7887



B17
LDGV
83-85
0.05674
0.00853










0.9868
0.9946
0.7356
1.5594
2.1184
0.6029
3.6309


B18
LDGV
83-85
0.04106
-0.0038









1
0.9461
1.0589
0.7763
1.1008
1.4725
0.8392
2.602
1.9484

B19
LDGV
83-85
0.03964
-0.00206










0.9706
1.0465
0.8189
0.9395
1.277
0.41
2.4945
1.6256
0.6907

-------

M147 Composite Regression Coefficients, HC, 1986 to 1989 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
86-89
0.31427
0.27681
15.5205


















P2
LDGV
86-89
0.23056
0.12883
1.6181
4.2149















.

P3
LDGV
86-89
0.19626
0.11002
2.0358
2.3971
3.9903
















P4
LDGV
86-89
0.18598
0.09989
0.6425
2.546
1.7035
3.3548















P5
LDGV
86-89
0.17584
0.09767
0.5819
1.852
1.5615
1.555
3.8429














P6
LDGV
86-89
0.16772
0.10204
-0.8376
1.9082
1.0601
1.0965
1.9459
3.8412













P7
LDGV
86-89
0.16444
0.09594
-0.6742
1.6725
0.9995
1.3082
0.7861
3.2631
2.3581












P8
LDGV
86-89
0.15806
0.08909
0.1422
1.5556
0.6844
0.9331
1.0208
2.2001
1.5379
2.5805











P9
LDGV
86-89
0.1519
0.08696
-0.1948
1.5757
0.7441
1.0414
0.6635
1.0744
1.3289
1.468
3.5915










P10
LDGV
86-89
0.14467
0.0797
-0.4894
1.5393
0.9657
0.7863
0.5873
1.2938
1.3345
1.1773
-0.2863
4.8853









P11
LDGV
86-89
0.11095
0.02972
0.2469
0.721
1.1728
0.8987
0.4133
1.9423
0.6973
0.4156
0.9936
2.4194
1.6263








P12
LDGV
86-89
0.09318
0.02564
0.8246
0.6275
0.7263
0.7966
0.542
1.6116
0.6663
0.672
0.4503
2.6883
1.1012
1.7602







P13
LDGV
86-89
0.06902
0.02184
1.3196
0.4061
0.8931
0.8959
0.5829
1.0567
0.7867
0.7859
0.5864
1.9088
0.9881
0.291
2.4099






P14
LDGV
86-89
0.06241
0.01809
1.4552
0.31
1.0344
0.9038
0.6073
1.022
0.7521
0.848
0.6425
1.8254
0.813
0.5647
1.1453
2.4731





P15
LDGV
86-89
0.05136
0.01585
1.2694
0.4095
0.9607
0.9108
0.6871
0.8398
0.7304
0.8585
0.5526
1.7815
0.6924
0.6284
0.7316
0.9325
2.1051




P16
LDGV
86-89
0.04861
0.01432
1.1846
0.4732
0.8917
0.9259
0.7055
0.8742
0.7619
0.7564
0.5578
1.5489
0.7021
0.6507
0.7862
0.9721
1.2367
2.5815



P17
LDGV
86-89
0.03332
0.00699
1.0682
0.673
0.7585
0.7037
0.8742
0.6284
0.7246
0.6208
0.7319
1.1727
0.7152
0.6517
0.7253
1.0692
1.1351
1.0816
2.2316


P18
LDGV
86-89
0.02685
0.0032
1.1443
0.6139
0.8508
0.7766
0.7017
0.7447
0.6277
0.623
0.6745
1.0469
0.7238
0.6588
0.7335
0.8575
0.8976
0.668
1.5797
1.3415

P19
LDGV
86-89
0.02176
0.00312
1.0887
0.6218
0.766
0.6703
0.7888
0.6802
0.7991
0.5738
0.7502
0.9406
0.7447
0.674
0.6947
0.6605
0.8184
0.1544
1.3072
0.9142
0.9739
IM14
\7 Phase 2 Regression Coefficients, HC, 1986 to 1989 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS *
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
86-89
0.17572
0.05913










2.9828








B12
LDGV
86-69
0.14079
0.04187










1.8474
2.837







B13
LDGV
86-89
0.10283
0.03308










1.4791
0.4901
3.5664






B14
LDGV
86-89
0.09666
0.02888










1.2557
0.8541
2.0611
2.9206





B15
LDGV
86-89
0.07943
0.02543










1.0638
0.9286
1.331
0.6468
3.2335




B16
LDGV
86-89
0.07243
0.02198










1.0486
0.9587
1.3534
0.8765
1.5227
4.8276



B17
LDGV
86-89
0.04689
0.01029










1.0457
0.8812
1.097
1.3615
1.4856
1.7603
3.2261


B18
LDGV
86-89
0.03789
0.00418










0.9974
0.9025
1.0724
1.0277
1.1846
1.1391
2.3711
1.8028

B19
LDGV
86-89
0.03036
0.00369










1.0225
0.8952
0.9808
0.8231
1.1
0.3076
1.8483
1.2733
1.3385

-------

M147 Composite Regression Coefficients, HC, 1990 to 1995 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10 :
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
90-95
0.2971
0.16705
15.7948


















P2
LDGV
90-95
0.19863
0.05505
-1.9351
4.8837

















P3
LDGV
90-95
0.16628
0.03974
-1.5889
2.5546
5.6414
















P4
LDGV
90-95
0.1584
0.03534
-1.9531
2.652
3.0556
3.5632















P5
LDGV
90-95
0.14861
0.0377
-1.7859
2.1399
2.3054
1.5953
3.8796














P6
LDGV
90-95
0.1428
0.04157
-2.1978
2.3528
1.3397
1.1611
1.6243
4.1795













P7
LDGV
90-95
0.13017
0.03906
-1.447
1.8386
1.3743
1.2125
-0.2829
2.1347
4.9626












P8
LDGV
90-95
0.12213
0.03722
-0.5405
1.6099
1.2031
0.3856
-0.1326
1.4892
3.3678
3.4765

]









P9
LDGV
90-95
0.11733
0.03754
-0.7341
1.7237
1.0051
0.4924
-0.9573
0.6324
3.2462
2.3004
3.9191










P10
LDGV
90-95
0.11526
0.0368
-0.8148'
1.6847
1.071
0.425
-1.176
0.876
2.845
2.502
1.4335
2.9572









P11
LDGV
90-95
0.08741
0.00996
0.6446
0.8311
1.0111
0.5067
-0.4113
1.2288
2.2281
1.2696
1.6991
1.3219
1.5051








P12
LDGV
90-95
0.07472
0.0105
0.6979
0.7429
0.706
0.5223
-0.0564
1.3101
1.9432
1.288
1.2908
1.3897
0.8836
1.8454







P13
LDGV
90-95
0.05916
0.0139
1.3045
0.4375
0.9242
0.6499
0.0689
1.2742
1.493
1.04
1.3582
0.4913
0.8544
0.3112
2.5569






P14
LDGV
90-95
0.05362
0.01549
1.386
0.3479
1.0903
0.5466
0.252
1.3522
1.424
0.6349
1.5658
0.679
0.6813
0.3943
1.6017
2.0376





P15
LDGV
90-95
0.04337
0.01025
1.2116
0.4485
0.9328
0.7036
0.5038
1.3664
1.1003
0.7903
1.3103
0.2473
0.6803
0.7135
0.7193
0.5976
2.3136




P16
LDGV
90-95
0.04152
0.00959
1.1338
0.4558
0.9892
0.8217
0.5423
1.2542
0.9416
0.7779
1.3289
0.1879
0.6808
0.7232
0.692
0.6981
1.451
2.8503



P17
LDGV
90-95
0.0313
0.00573
0.9123
0.6301
0.5621
0.8487
0.6701
1.1772
0.8709
0.7111
1.0362
0.425
0.686
0.7574
0.6663
0.8447
1.2224
1.3549
2.4338


P18
LDGV
90-95
0.02304
0.004
1.0116
0.6089
0.6873
0.6706
0.6772
1.1438
0.7472
0.6904
0.8929
0.5431
0.6856
0.7093
0.6728
0.682
1.042
0.6819
1.3735
1.591

P19
LDGV
90-95
0.01929
0.00299
0.874
0.6682
0.6242
0.6993
0.6995
1.2226
0.7716
0.6058
0.6359
0.606
0.6915
0.724
0.6677
0.6623
0.8334
0.1215
1.1562
1.238
0.9297
IM14
\7 Phase 2 Regression Coefficients, HC, 1990 to 1995 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
'i
C10 \
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
90-95
0.13911
0.0203










2.9778








B12
LDGV
90-95
0.11712
0.01783










1.7491
2.9654







B13
LDGV
90-95
0.08624
0.01945










1.2881
0.3333
3.9332






B14
LDGV
90-95
0.08064
0.02126










1.0669
0.4262
2.8321
2.3928





B15
LDGV
90-95
0.06433
0.0142










1.0083
0.9588
1.2508
0.4227
3.4379




B16
LDGV
90-95
0.06126
0.01375










0.9905
0.9989
1.1536
0.6257
2.0703
4.3157



B17
LDGV
90-95
0.04442
0.00749










0.9692
0.997
1.0495
1.0206
1.6198
2.0719
3.5584


B18
LDGV
90-95
0.03245
0.00457










0.9306
0.9602
0.9645
0.8007
1.3774
1.0699
1.9911
2.2266

B19
LDGV
90-95
0.02708
0.00366










0.9309
0.9723
0.9484
0.8169
1.0622
0.3307
1.6177
1.7198
1.2796

-------
IM147 Composite Regression Coefficients, HC, 1996 and Newer Model Year LDGV's





Regression Coefficients
Segment
Number
(

RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
96+
0.10038
0.04026
29.2462
















.

P2
LDGV
96+
0.07371
0.01788
5.9182
4.0658

















P3
LDGV
96+
0.06844
0.01874
2.9638
1.9584
5.5186
















P4
LDGV
96+
0.06132
0.01973
2.7332
1.6433
3.0543
4.4161















P5
LDGV
96+
0.06046
0.02162
2.8327
1.3424
2.314
3.7839
1.694














P6
LDGV
96+
0.05435
0.02387
1.2378
1.2522
0.457
3.4931
-1.2143
7.4758













P7
LDGV
96+
0.05447
0.0241
1.0091
1.2788
0.4429
3.4792
-1.2783
7.8471
-0.2285












P8
LDGV
96+
0.05359
0.02337
0.9941
0.7729
0.7794
3.2658
-2.1201
7.6602
-0.5933
3.1409









.

P9
LDGV
96+
0.05279
0.02482
2.6229
0.6273
0.6458
3.3195
-1.1123
4.6464
-0.1966
0.788
4.1995










P10
LDGV
96+
0.05143
0.02476
1.0359
0.4556
1.1796
3.0156
-1.3707
3.8218
0.108
0.823
-0.5809
6.8862









P11
LDGV
96+
0.03867
0.00936
5.4115
-0.1934
1.9861
0.9553
-0.8572
0.8886
0.8704
-0.1062
0.029
6.6498
1.6503








P12
LDGV
96+
0.02114
0.009
3.1864
0.1934
1.3914
1.1882
0.032
-0.1178
0.7255
1.0187
0.0701
4.5106
0.6495
2.1154





.

P13
LDGV
96+
0.01901
0.00675
3.9129
0.3208
1.0647
0.8952
0.4296
0.6088
0.9736
0.1471
-0.7025
3.5491
0.6163
1.6571
1.3153






P14
LDGV
96+
0.01746
0.00454
4.3463
0.3067
1.0782
1.0439
0.3355
1.3018
1.0886
-0.2129
-0.7694
2.9793
0.5207
1.3494
1.2261
1.5726





P15
LDGV
96+
0.01604
0.00595
4.6859
0.2669
0.8615
0.9562
0.5265
1.8859
0.9556
-0.0501
-1.2322
2.431
0.5884
1.2496
1.3707
-0.6015
1.5225




P16
LDGV
96+
0.01579
0.00648
4.6016
0.2309
0.8473
0.9826
0.3155
2.3189
0.8167
0.4521
-1.291
2.0816
0.5345
1.4982
1.0515
-0.1967
2.1338
-2.9193



P17
LDGV
96+
0.01236
0.00351
2.9817
0.5075
0.6011
1.1216
0.3319
1.6187
0.8518
0.5096
0.0325
1.425
0.5667
1.2205
0.6224
1.0067
1.5045
-2.3679
2.3031


P18
LDGV
96+
0.00892
0.00122
2.9957
0.5568
0.7792
0.788
0.2152
1.8089
0.6335
0.4301
0.2697
0.9495
0.5389
1.1609
0.7993
0.5202
1.4602
-1.9165
1.033
1.7225

P19
LOGV
96+
0.00643
0.00092
1.469
0.8076
0.4864
0.7865
0.6192
1.1938
0.534
0.2013
0.8084
1.3759
0.6416
0.824
0.4897
0.7037
0.7724
0.0797
0.3811
1.5812
0.8653
IM147 Phase 2 Regression Coefficients, HC, 1996 and Newer Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
96+
0.06231
0.01367










2.7246








B12
LDGV
96+
0.03471
0.01289










1.0956
3.2189







B13
LDGV
96+
0.02909
0.00972










0.7958
2.4462
2.013






B14
LOGV
96+
0.02839
0.00805










0.7353
2.2615
1.8431
1.2834





B15
LDGV
96+
0.02692
0.00988










0.8024
2.2022
1.7552
-1.4225
1.8689




B16
LDGV
96+
0.02668
0.01042










0.7459
2.4894
1.4031
-0.8725
2.5744
-3.5714



B17
LOGV
96+
0.0192
0.00442










0.8885
1.711
0.9711
0.9804
1.8655
-2.5604
3.883


B18
LDGV
96+
0.01449
0.00185










0.7232
1.6635
1.0171
0.5616
1.7084
-1.9289
2.0901
2.3918

B19
LDGV
96+
0.01043
0.00074










0.92
1.1468
0.7967
0.6882
1.2025
-0.0572
0.6503
2.302
1.1734

-------
IM147 Composite Regression Coefficients, CO, 1981 to 1985 Model Year LDGH's
Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT1
81-85
10.1754
14.5276
12.5442


















P2
LDGT1
81-85
7.53286
8.22078
5.3429
3.5563

















P3
LDGT1
81-85
6.63268
6.60978
2.7905
2.1525
5.3312
















P4
LDGT1
81-85
6.52317
6.63083
1.5125
2.2786
3.2782
3.8618















P5
LDGT1
81-85
6.508
6.69361
1.2349
2.1748
2.865
3.4811
1.0432














P6
LDGT1
81-85
6.43582
6.71221
0.1813
2.3646
2.9243
1.5307
0.1722
2.7427













P7
LDGT1
81-85
6.19445
6.37324
0.5635
1.9693
2.7172
0.6361
-1.561
3.2843
2.9955












P8
LDGT1
81-85
5.94027
5.98064
1.2119
1.6393
2.7167
0.5356
-1.4442
1.3759
1.7876
3.6004











P9
LDGT1
81-85
5.9353
5.97383
1.0059
1.6737
2.5314
0.6549
-1.7102
0.8508
1.7967
3.4358
1.1942










P10
LDGT1
81-85
5.85738
6.02848
1.177
1.7285
2.0533
0.7067
-2.0802
1.0202
1.2427
3.6953
-1.2444
3.1472









P11
LDGT1
81-85
4.17486
2.67355
0.0251
1.2531
0.5349
0.8814
-0.4647
2.1824
0.0334
2.27
1.2267
2.243
1.0397








P12
LDGT1
81-85
3.65635
1.97698
-0.5472
1.2512
-0.2635
2.0162
-0.5276
2.225
0.3841
1.8143
0.5359
2.6593
0.7892
1.1045







P13
LDGT1
81-85
2.30258
0.92158
0.7798
0.5868
0.3445
2.2218
0.3028
1.3536
0.523
1.1833
0.7632
1.2554
0.7979
0.6638
2.4441






P14
LDGT1
81-85
1.68789
0.51912
1.7468
0.429
0.6515
1.5816
0.7182
0.8416
0.48
1.2506
1.093
1.1172
0.6993
0.5859
1.7776
1.5319





P15
LDGT1
81-85
1.13559
0.36037
1.4986
0.481
0.5203
1.5061
0.696
0.646
0.8751
0.9462
0.7302
1.215
0.6963
0.7448
0.985
0.9478
1.4391




P16
LDGT1
81-85
1.03437
0.37532
1.4149
0.5205
0.4715
1.4258
0.7753
0.6646
0.8775
0.7646
0.5543
1.4051
0.6989
0.7734
0.9487
0.9689
0.8955
1.6125



P17
LDGT1
81-85
0.96568
0.20115
1.2423
0.5923
0.2634
1.3236
0.9825
0.7034
0.7693
0.7682
0.1979
1.3783
0.7136
0.7407
0.9773
0.9693
0.8911
1.2362
1.2653


P18
LDGT1
81-85
0.75949
0.06889
1.1529
0.5685
0.4385
1.1423
0.7942
0.9943
0.7711
0.6846
0.5815
1.031
0.7017
0.7499
0.8098
0.8097
0.847
0.846
1.2494
0.9829

P19
LDGT1
81-85
0.34973
-0.06051
1.1508
0.6559
0.4115
1.1472
0.7599
0.9619
0.799
0.6744
0.5327
0.985
0.7106
0.7294
0.7378
0.6891
0.6385
0.7063
1.0232
0.6907
0.8306


IM147 Phase 2 Regression Coefficients, CO, 1981 to 1985 Model Year LDGH's





Repression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT1
81-85
9.2129
9.11082










1.5105








B12
LDGT1
81-85
5.96613
4.72873










1.1535
1.7914







B13
LDGT1
81-85
3.55873
1.89825










1.0491
0.955
3.6598






B14
LDGT1
81-85
2.92833
1.4436










0.9077
0.9194
2.7661
1.929





B15
LDGT1
81-85
1.93926
0.86159










0.9453
1.0943
1.32
1.0292
2.3486




B16
LDGT1
81-85
1.76086
0.83361










0.9517
1.1151
1.2896
1.1023
1.3595
2.6847



B17
LDGT1
81-85
1.44658
0.21699










0.9768
0.9803
1.3491
1.2314
1.27
1.5265
2.526


B18
LDGT1
81-85
1.172
0.01644










0.9407
1.0108
1.0591
1.0119
1.2102
1.0895
2.58
1.3347

B19
LDGT1
81-85
0.69787
-0.12536










0.9656
0.9797
0.9936
0.8578
0.9082
0.8798
2.1293
0.9645
1.1513

-------
IM147 Composite Regression Coefficients, CO, 1986 to 1989 Model Year LDGH's
Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT1
86-89
7.18532
10.8086
9.9102


















P2
LDGT1
86-89
5.68789
7.0459
1.8741
2.6779

















P3
LDGT1
86-89
5.04319
5.5558
1.7455
1.371
5.3393
















P4
LDGT1
86-89
4.88921
5.13459
1.4696
1.5629
2.9026
5.3333















P5
LDGT1
86-89
4.78915
5.08892
1.6656
1.4035
2.0371
4.1723
2.0022














P6
LDGT1
86-89
4.74332
5.11226
1.0229
1.4755
1.9866
3.8059
1.0898
2.4375













P7
LDGT1
86-89
4.65225
4.91955
1.7462
1.3554
1.7097
3.8418
-0.0053
1.9673
1.6899












P8
LDGT1
86-89
4.48557
4.61047
2.0142
1.3439
1.7523
2.7365
-0.09
0.1646
0.9526
2.9249











P9
LDGT1
86-89
4.44811
4.5639
2.1382
1.316
1.6099
2.7676
-0.2399
-0.7903
0.9655
2.4766
2.079










P10
LDGT1
86-89
4.41872
4.48084
2.0022
1.3046
1.5957
2.4537
-0.4021
-0.7631
0.7899
2.5819
0.8104
2.0187









P11
LDGT1
86-89
3.05161
2.26609
2.8544
0.9359
0.8115
1.4536
0.41
0.3277
0.4047
2.1497
-0.2782
2.0562
0.9911








P12
LDGT1
86-89
2.51613
1.51088
2.8342
0.7464
0.9245
1.9441
0.5707
-0.2141
0.4114
2.0168
0.2073
1.9253
0.7438
1.2083







P13
LDGT1
86-89
1.68355
0.8064
2.1795
0.6233
0.8001
1.3103
0.4715
0.7177
0.6871
0.7545
0.7656
1.1501
0.7503
0.8943
2.2692






P14
LDGT1
86-89
1.31741
0.68081
2.1784
0.5849
0.6912
1.3598
0.4357
0.9373
0.6956
0.8278
0.6331
1.2087
0.7057
0.7686
1.824
1.1207





P15
LDGT1
86-89
0.93911
0.47
1.8779
0.6014
0.6301
1.3592
0.4395
0.8121
0.876
0.6638
0.7005
0.9787
0.7117
0.7437
1.0888
0.8731
1.3809




P16
LDGT1
86-89
0.88497
0.41787
1.6764
0,5971
0.6595
1.3638
0.5037
0.8323
0.8426
0.6198
0.6814
1.0802
0.7147
0.7671
1.022
0.8818
0.9654
1.3933



P17
LDGT1
86-89
0.76094
0.28167
1.1884
0.633
0.6369
1.0655
0.6729
0.7423
0.7527
0.7112
0.7965
0.8042
0.7055
0.7447
0.9245
0.8862
1.048
0.6815
1.7386


P18
LDGT1
86-89
0.51513
0.11517
1.107
0.6616
0.6251
1.1599
0.7126
0.7927
0.5228
0.8379
0.7639
0.7464
0.7046
0.7204
0.7302
0.7811
1.0809
0.4756
1.39
0.889

P19
LDGT1
86-89
0.22775
0.06824
0.9804
0.6792
0.6446
1.0212
0.6645
0.7468
0.6847
0.7006
0.7495
0.7734
0.7063
0.7111
0.7207
0.7067
0.7928
0.5475
0.9726
0.7062
0.7346







IM147 Phase 2 Regression Coefficients,
CO,
1986 to 1989 Model Year LDGTI's








Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT1
86-89
5.73419
6.44585










1.4889








B12
LDGT1
86-89
3.83261
3.46613










1.1555
1.6694







B13
LDGT1
86-89
2.36328
1.33398










1.0419
1.1807
3.2911






B14
LDGT1
86-89
1.90917
1.13186










0.9737
1.0107
2.6834
1.4946





B15
LDGT1
86-89
1.36531
0.75413










0.976
0.9839
1.5355
1.1464
1.973




B16
LDGT1
86-89
1.28845
0.69885










0.9841
1.0114
1.4453
1.1588
1.3867
1.9667



B17
LDGT1
86-89
1.04812
0.39116










0.9599
0.9873
1.256
1.1763
1.4736
0.8337
2.6609


B18
LDGT1
86-89
0.7463
0.14799










0.9544
0.9576
1.0029
1.0365
1.5243
0.6229
2.1332
1.1447

B19
LDGT1
86-89
0.33544
0.07788










0.9581
0.9522
0.9677
0.9398
1.0857
0.7154
1.4816
0.9344
1.0357

-------
1
M147 Composite Regression Coefficients, CO, 1990 to 1995 Model Year LDGTI's





Repression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
!|
C10 ,,
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT1
90-95
7.96708
6.47319
8.6737








I!









P2
LDGT1
90-95
5.93561
3.60059
0.3029
2.9243







f









P3
LDGT1
90-95
5.49867
2.58569
0.1967
1.6676
5.73
















P4
LDGT1
90-95
5.47555
2.41839
0.0472
1.683
4.5333
2.9942















P5
LDGT1
90-95
5.34097
2.4681
0.3008
1.3842
2.8744
1.4949
3.7142




1









P6
LDGT1
90-95
5.23567
2.50459
-0.4457
1.3784
2.6883
0.8341
2.2673
4.9505



r1









P7
LDGT1
90-95
5.12065
2.31146
-0.3312
1.2837
1.873
-0.1146
0.7803
4.7671
3.2802












P8
LDGT1
90-95
4.93002
2.15394
-0.1308
1.2614
1.6587
-0.7666
0.4112
1.9844
1.3783
5.3488






..




P9
LDGT1
90-95
4.92436
2.15209
-0.2521
1.2934
1.4663
-0.6767
0.1855
1.6357
1.4433
4.9503
1.3627










P10
LDGT1
90-95
4.83877
2.07448
-0.0883
1.3418
1.2572
-0.6746
-0.0356
1.4432
1.0559
4.4422
-1.7507
4.57









P11
LDGT1
90-95
3.05447
0.67888
0.7448
0.9163
0.7304
-1.1973
0.6625
1.9991
1.1363
2.2194
-0.3005
2.3958
1.1499








P12
LDGT1
90-95
2.59434
0.35922
0.8716
0.834
-0.2278
-0.1746
0.685
1.603
1.4913
1.7526
-0.5786
2.4993
0.9124
1.5835







P13
LDGT1
90-95
1.69051
0.3538
0.7846
0.7978
0.2453
0.7291
0.7109
0.6836
0.9317
0.9732
0.7569
0.3609
0.8116
0.8909
2.4633






P14
LDGT1
90-95
1.25118
0.29289
0.7084
0.7156
0.4878
0.5609
0.6826
0.8354
0.9735
1.1145
0.55
0.9761
0.7373
0.7493
1.6233
1.1532





P15
LDGT1
90-95
0.88666
0.17653
0.6832
0.7443
0.6247
0.7585
0.551
0.8411
0.8994
0.6594
0.516
0.6847
0.7688
0.6805
1.0955
0.8841
1.4675




P16
LDGT1
90-95
0.83688
0.14342
0.6546
0.7325
0.6626
0.7804
0.6
0.754
0.9659
0.5296
0.6743
0.5828
0.7741
0.6855
0.9886
0.964
1.0266
1.788



P17
LDGT1
90-95
0.75939
0.03084
0.6751
0.7626
0.4984
0.7727
0.6819
0.6512
0.8746
0.5588
0.7025
0.4405
0.774
0.6594
0.9682
0.9724
1.0041
1.3254
2.054


P18
LDGT1
90-95
0.48016
0.02847
0.6665
0.7378
0.5678
0.7356
0.81
0.7878
0.6105
0.7172
0.7969
0.5663
0.7101
0.721
0.8057
0.915
0.6348
1.2367
1.3997
1.0199

P19
LDGT1
90-95
0.16891
-0.00569
0.7401
0.7243
0.6471
0.7729
0.6775
0.7704
0.7643
0.6818
0.7573
0.6839
0.7157
0.7187
0.7375
0.6868
0.717
0.6511
1.1384
0.7248
0.7498
IM147 Phase 2 Regression Coefficients, CO, 1990 to 1995 Model Year LDGH's





Repression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10 1
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT1
90-95
5.48884
2.92303










1.7403








B12
LDGT1
90-95
3.74619
0.83254










1.3383
2.3792







B13 '
LDGT1
90-95
2.29939
0.47273










1.1019
1.1903
3.3532






B14
LDGT1
90-95
1.7181
0.48708









'1
1.0162
1.02
2.3871
1.5186





B15
LDGT1
90-95
1.20254
0.23609










1.0388
0.9035
1.4703
1.2205
1.9615




B16
LDGT1
90-95
1.13572
0.20105










1.0447
0.9125
1.3168
1.3287
1.3674
2.3837



B17
LDGT1
90-95
1.04495
0.01286










1.0374
0.8678
1.2457
1.3709
1.2968
1.8468
2.5009


B18
LDGT1
90-95
0.65189
0.0106










0.9551
0.9717
1.0606
1.2615
0.8351
1.7471
1.7371
1.3845

B19
LDGT1
90-95
0.219
0.00146










-0.968
0.9695
0.9969
0.9364
0.9681
0.9047
1.5264
0.9898
1.012

-------
IM147 Composite Regression Coefficients, CO, 1996 and Newer Model Year LDGTI's
Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT1
96+
1.82409
1.39419
5.531


















P2
LDGT1
96+
1.63165
0.95561
-5.9413
3.0869

















P3
LDGT1
96+
1.57733
0.85422
-1.5783
1.5971
3.8856
















P4
LDGT1
96+
1.57696
0.84862
-1.5432
1.758
1.4232
4.1058















P5
LDGT1
96+
1.55655
0.77387
-1.8497
1.5252
1.4375
1.5669
3.3072














P6
LDGT1
96+
1.48358
0.83228
-5.5406
1.6396
-0.2011
-2.3153
21554
10.6111













P7
LDGT1
96+
1.47504
0.7148
-5.2472
1.8872
-0.9914
-0.8568
0.4965
7.7306
2.5842












P8
LDGT1
96+
1.48311
0.71859
-5.1507
1.8553
-0.9277
-0.9266
0.671
8.0227
2.621
-0.2793











P9
LDGT1
96+
1.49155
0.72084
-5.0954
1.8331
-1.0008
-0.6946
0.7565
7.9469
2.63
-0.1935
-0.3333










P10
LDGT1
96+
1.48528
0.70223
-3.9278
1.5364
-0.9574
-0.075
1.1515
8.3687
2.439
-0.1927
-2.1271
1.4542









P11
LDGT1
96+
0.71796
0.22242
-1.2984
1.4739
0.5359
-1.1903
0.4023
2.2155
2.7505
0.2649
0.6008
1.0433
0.8928








P12
LDGT1
96+
0.54429
0.13226
1.524
1.022
0.7868
-1.432
0.1372
2.482
1.6269
-0.3682
2.2202
0.4979
0.8781
1.6265







P13
LDGT1
96+
0.43766
0.08336
1.4971
0.7999
1.0816
-1.4102
0.8701
2.4778
1.5114
-0.0111
0.8915
0.5019
0.8812
0.3603
3.2131






P14
LDGT1
96+
0.27254
0.0707
1.3293
0.9437
0.7684
-0.6826
0.5074
1.8189
1.5858
0.2076
0.9221
0.5798
0.729
0.6284
2.4591
0.7018





P15
LDGT1
96+
0.19651
0.03007
1.3733
0.8799
0.3876
0.2025
0.6389
2.4824
0.9733
0.4929
0.3408
0.8102
0.7261
0.7297
1.4475
0.6703
1.466




P16
LDGT1
96+
0.17615
0.02979
1.0484
0.8101
0.7323
-0.205
0.5366
2.4872
0.7994
0.4735
0.5618
0.7865
0.7275
0.7712
1.4017
0.6818
0.9833
2.2195



P17
LDGT1
96+
0.13011
0.05988
1.062
0.6811
0.3816
0.7616
0.4795
2.2042
0.9584
0.5832
0.1538
0.8447
0.7233
0.7198
1.1616
0.6987
1.0372
0.2683
1.8782


P18
LDGT1
96+
0.08591
0.02837
1.3696
0.6559
0.7295
0.2618
0.8191
1.2599
0.5828
0.7744
0.6755
0.6858
0.712
0.7269
0.7236
0.7111
0.9386
0.4881
1.7121
1.0654

P19
LDGT1
96+
0.03308
0.00956
0.6181
0.7187
0.7481
0.61
0.6377
0.8223
0.7051
0.6935
0.8481
0.6648
0.7101
0.7232
0.6944
0.7189
0.8125
0.226
1.1367
0.7471
0.7019


IM147
Phase 2 Repression Coefficients, CO, 1996 and Newer Model Year LDGTI's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT1
96+
1.23604
0.96823










0.9526








B12
LDGT1
96+
0.74363
0.24386










1.1933
2.1843







B13
LDGT1
96+
0.60473
0.1527










1.2056
0.5562
4.3384






B14
LDGT1
96+
0.40405
0.15643










0.9921
0.9282
3.3758
0.9673





B15
LDGT1
96+
0.30462
0.06538










0.9969
1.0801
1.9431
0.914
2.0105




B16
LDGT1
96+
0.26097
0.03614










0.9954
1.08
1.8842
0.9365
1.1866
3.7438



B17
LDGT1
96+
0.2048
0.0653










0.9914
1.0331
1.5352
0.9586
1.2475
1.026
2.448


B18
LDGT1
96+
0.12661
0.03391










0.9631
0.9575
0.9469
0.9698
1.2044
0.8083
2.4102
1.4239

B19
LDGT1
96+
0.04352
0.01126










0.9604
0.9595
0.9395
0.9708
1.1345
0.2607
1.4873
1.0202
0.9453

-------
1
M147 Composite Repression Coefficients, CO, 1981 to 1985 Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT2
81-85
18.0705
25.7173
7.5612








!









P2
LDGT2
81-85
12.3795
12.9451
3.4376
3.6374







.









P3
LDGT2
81-B5
11.5598
10.6148
1.0212
2.3546
5.6508






.









P4
LDGT2
81-85
11.1259
9.72659
-0.5375
2.5842
2.5322
5.5716















P5
LDGT2
81-85
11.1623
9.79162
-0.516
2.5693
2.415
5.44
0.2344




'1









P6
LDGT2
81-85
10.5815
10.5149
-5.1729
2.5377
2.4642
3.5854
-2.439
9.5373













P7
LDGT2
81-85
10.5066
9.63447
-5.1298
2.3487
2.1758
3.1663
-2.7919
8.8001
1.9586












P8
LDGT2
81-85
10.4986
9.46232
-4.5397
2.2916
2.2264
2.9027
-2.935
8.1749
1.56
1.182

.









P9
LDGT2
81-85
10.534
9.45853
-4.6546
2.2745
2.3176
2.9007
-2.96
8.3683
1.5537
1.2902
-0.3956








.

P10
LDGT2
81-85
10.5104
9.14764
-4.0569
2.3269
1.7791
3.0871
-3.3367
7.9403
1.0377
1.7694
-1.8655
2.3687









P11
LDGT2
81-5
6.70683
1.00692
-0.9625
0.8295
2.9212
0.599
-1.3472
6.2535
1.0658
1.6592
-0.2765
1.4949
1.0962








P12
LDGT2
81-85
5.84441
1.64077
-0.9579
0.8376
2.0719
1.2901
-1.7228
6.1373
1.3423
1.3993
-0.7286
1.7599
0.6956
1.5499







P13
LDGT2
81-85
3.26892
0.61651
1.3225
0.6113
0.8575
1.1276
0.3105
2.4597
0.7224
0.0984
0.8037
1.0488
0.8185
0.7462
2.8417






P14
LDGT2
81-85
2.22826
0.53462
1.2795
0.6654
0.9477
1.2845
0.5586
1.7615
0.7222
0.4197
1.0753
0.8413
0.6372
0.838
1.8815
1.2908





P15
LDGT2
81-85
1.43068
0.16253
1.4763
0.6916
0.8805
1.0498
0.7614
1.593
0.6161
0.2931
0.9481
0.6408
0.759
0.6649
1.0119
0.9471
1.4604




P16
LDGT2
81-85
1.39761
0.09202
1.4913
0.6618
0.9629
0.9832
0.8115
1.4561
0.5963
0.4052
0.7374
0.7242
0.7696
0.6414
0.9815
0.9546
1.1597
1.2077



P17
LDGT2
81-85
1.30226
-0.04105
1.2639
0.683
0.8813
0.8942
0.7966
1.641
0.6092
0.4611
0.6586
0.3214
0.791
0.5936
0.982
0.9715
0.9473
1.1888
1.5709


P18
LDGT2
81-85
0.98169
-0.23459
1.3573
0.7081
0.8658
0.8401
0.6551
1.4524
0.5751
0.6074
0.7206
0.4241
0.7757
0.6063
0.7425
0.8257
0.8673
1.0036
1.3323
0.8645

P19
LDGT2
81-85
0.50955
-0.1442
1.2761
0.7138
0.6615
0.8723
0.6415
1.4442
0.6884
0.5997
0.8506
0.4704
0.7392
0.6704
0.7048
0.6711
0.7256
0.4192
1.1487
0.8223
0.7477
IM147 Phase 2 Regression Coefficients, CO, 1981 to 1985 Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT2
81-85
13.6662
10.3119










1.5327








B12
LDGT2
81-85
9.5454
6.3219









'1
0.9685
2.6838







B13
LDGT2
81-85
4.74433
1.25606










1.0757
0.9968
4.0692






B14
LDGT2
81-85
3.50162
1.57213









'
0.8395
1.1681
2.9369
1.7248





B15
LDGT2
81-85
2.41832
0.59238









.1
1.0109
0.918
1.4255
1.2686
2.1564




B16
LDGT2
81-85
2.31489
0.51543









.
1.0284
0.8761
1.3639
1.2777
1.5125
2.5385



B17
LDGT2
81-85
2.02742
-0.13816









i
1.0611
0.7865
1.3133
1.3648
1.0331
2.4287
2.7411


B18
LDGT2
81-85
1.59498
-0.3696










1.0435
0.8038
0.976
1.1376
0.9439
2.1064
2.2992
1.2758

B19
LDGT2
81-85
1.13333
-0.2565










1
0.8755
0.9246
0.9223
0.7526
1.3895
2.0596
1.205
1.0168

-------
IM147 Composite Regression Coefficients, CO, 1986 to 1987 Model Year LDGT2's
Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT2
86-87
10.9444
12.9835
9.9921


















P2
LDGT2
86-87
7.55854
7.97406
5.9552
2.7507

















P3
LDGT2
86-87
6.99501
6.13276
4.0152
1.8868
4.4945
















P4
LDGT2
86-87
6.97238
5.95324
3.4929
1.8483
3.4182
2.3575















P5
LOGT2
86-87
6.96833
6.05169
3.0277
1.6681
2.9579
1.9105
1.5754














P6
LDGT2
86-87
6.92215
5.98054
2.189
1.8432
2.2804
1.5316
0.9365
3.0663













P7
LDGT2
86-87
6.94695
5.96146
2.2344
1.7997
2.1735
1.5149
0.7182
3.2075
0.349












P8
LDGT2
86-87
6.86254
5.92929
3.5275
1.4609
2.7997
0.5921
0.3135
2.0879
-0.2127
2.0942











P9
LDGT2
86-87
6.87158
5.95327
3.8626
1.5007
2.5734
0.597
0.1327
1.6367
-0.0355
1.5977
1.3024










P10
LDGT2
86-87
6.8465
5.84494
3.819
1.5958
2.1327
0.6932
0.2752
1.0075
-0.5524
1.789
-0.4389
2.5615









P11
LDGT2
86-87
4.00509
1.79665
1.9731
0.449
2.1242
-1.7461
2.0304
1.3946
-0.7103
1.246
1.7513
2.4635
1.0775








P12
LDGT2
86-87
3.20981
1.45021
1.8521
0.2977
1.3666
1.06
2.0745
-0.8491
0.2216
1.3232
1.1353
1.6136
0.6802
1.8785







P13
LDGT2
86-87
2.18993
0.93828
1.6364
0.6089
1.2938
1.9515
0.3685
-0.5328
-0.0587
1.2006
0.4256
1.2367
0.6922
1.2207
2.0554






P14
LDGT2
86-87
1.37534
0.49488
1.2757
0.6254
1.0665
0.9774
. 0.576
0.938
0.198
1.1141
0.5629
1.3587
0.6742
0.7466
1.5811
1.3798





P15
LDGT2
86-87
0.82014
0.15633
1.0946
0.6111
0.9745
0.7593
0.7578
1.1728
0.4226
1.0261
0.3048
1.0888
0.7145
0.6425
0.9896
1.02
1.5309




P16
LDGT2
86-87
0.69514
-0.03757
1.0745
0.6025
1.0009
0.7342
0.6475
1.2043
0.645
0,9395
0.3972
0.8668
0.7284
0.6071
0.9474
1.1426
0.9553
2.2245



P17
LDGT2
86-87
0.60214
-0.0821
1.1799
0.6279
0.8864
0.6465
0.8573
0.9847
0.4956
1.0034
0.4603
0.6907
0.7232
0.6711
0.8613
1.1323
0.9966
1.4224
1.7537


P18
LDGT2
86-87
0.44333
-0.03364
1.1598
0.6606
0.7253
0.5353
0.9864
1.1754
0.4065
0.9965
0.5489
0.8032
0.7002
0.7326
0.6569
1.0226
0.9838
0.6221
1.7755
0.6944

P19
LDGT2
86-87
0.18065
-0.04526
1.0637
0.7123
0.7409
0.8109
0.662
0.9665
0.652
0.7498
0.6309
0.6545
0.715
0.7245
0.7123
0.6921
0.7437
0.6817
1.153
0.6878
0.7348

IM147 Phase 2 Regression Coefficients, CO, 1986 to 1987 Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT2
86-87
7.78582
5.80047










1.4386








B12
LDGT2
86-87
4.68466
3.16394










0.9308
2.6872







B13
LDGT2
86-87
3.05474
1.67487










0.9606
1.5982
2.7862






B14
LDGT2
86-87
2.02059
1.1291










0.8908
1.1326
2.3405
1.8259





B15
LDGT2
86-87
1.24999
0.47151










0.9544
0.9442
1.4229
1.3063
2.1977




B16
LDGT2
86-87
1.06317
0.16747










0.9833
0.8605
1.3578
1.508
1.2803
3.3341



B17
LDGT2
86-87
0.90457
-0.06369










0.9757
0.9354
1.2019
1.5292
1.2917
2.1953
2.4754


B18
LDGT2
86-87
0.76129
-0.02736










0.9433
1.0434
0.9962
1.3721
1.2582
1.4319
2.4588
0.8181

B19
LDGT2
86-87
0.34719
-0.0285










0.9787
0.9885
0.9663
0.8916
0.9566
0.9606
1.5579
0.947
1.0918

-------
IM147 Composite Regression Coefficients, CO, 1988 to 1995 Model Year LDGT2's
Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT2
88-95
7.29843
8.32241
9.6375


















P2
LDGT2
88-95
5.72678
5.74122
-0.9189
2.3772

















P3
LDGT2
88-95
5.24439
4.67052
-1.3811
1.1541
5.5635
















P4
LDGT2
88-95
5.14826
4.26813
-1.2272
1.0883
3.6162
4.9841















P5
LDGT2
88-95
5.09801
4.25023
-1.2746
0.9561
3.0791
3.914
1.7536














P6
LDGT2
88-95
5.03227
4.30487
-2.116
1.045
2.75
2.5651
1.1445
4.8242













P7
LDGT2
88-95
4.94785
3.9903
-1.6248
0.9734
2.2915
2.261
0.0539
4.0718
2.4087












P8
LDGT2
88-95
4.89866
3.89264
-1.7403
1.0233
1.931
1.7839
0.2408
2.7207
1.5017
2.1928











P9
LDGT2
88-95
4.85108
3.81187
-1.2151
1.0106
1.7096
1.9917
-0.0267
1.4417
1.4322
1.6155
3.0089










P10
LDGT2
88-95
4.82605
3.68452
-1.0168
1.0461
1.7253
1.7644
-0.2401
1.2295
1.1312
1.6015
1.909
2.3455









P11
LDGT2
88-95
3.00685
1.74476
1.0382
0.6435
2.2076
0.8361
0.297
0.8814
1.2011
-0.12
2.4158
1.0536
1.0849








P12
LDGT2
88-95
2.71202
1.40261
1.3589
0.5437
1.8138
1.0668
0.3264
0.7761
1.4578
0.0633
1.6352
0.8125
0.9029
1.3647







P13
LDGT2
88-95
1.9294
0.60523
1.2096
0.6003
1.3401
0.909
0.5644
1.2411
0.8508
0.3162
0.575
0.1662
0.8851
0.7097
2.7113






P14
LDGT2
88-95
1.26478
0.42753
1.2507
0.6101
1.151
0.906
0.608
1.707
0.8161
0.593
0.3194
0.7475
0.7497
0.8072
1.7136
1.2385





P15
LDGT2
88-95
0.8779
0.28841
0.9697
0.6415
1.0695
0.7999
0.5885
1.284
0.8121
0.5778
0.5467
0.957
0.7335
0.8178
1.057
0.8384
1.2288




P16
LDGT2
88-95
0.79251
0.18425
1.0108
0.6516
1.0689
0.8061
0.5356
1.3495
0.8301
0.6371
0.4534
0.9417
0.7116
0.8113
1.0192
0.8943
0.9962
1.2531



P17
LDGT2
88-95
0.69731
0.07985
0.8509
0.7135
0.8792
0.7703
0.5294
1.3068
0.6755
0.6246
0.4121
0.958
0.7118
0.8086
0.9187
0.9121
0.9794
0.751
2.056


P18
LDGT2
88-95
0.52944
-0.00314
0.7855
0.7161
0.8485
0.6381
0.64
1.2114
0.5735
0.7301
0.4666
0.8616
0.7036
0.7599
0.6897
0.8686
0.9015
0.8581
1.2682
1.0027

P19
LDGT2
88-95
0.18228
-0.02029
1.0068
0.7103
0.7155
0.8232
0.7148
0.8349
0.6247
0.8302
0.6614
0.7366
0.7094
0.724
0.6921
0.7083
0.7232
0.6986
1.0875
0.7309
0.7311


IM147 Phase 2 Regression Coefficients, CO, 1988 to 1995 Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT2
88-95
5.55431
4.86126










1.4596








B12
LDGT2
88-95
3.81315
2.54349










1.2451
2.0945







B13
LDGT2
88-95
2.63707
0.83295










1.1848
0.9524
3.6628






B14
LDGT2
88-95
1.77124
0.72633










1.0256
1.1004
2.4663
1.6377





B15
LDGT2
88-95
1.22283
0.50627










1.0014
1.1189
1.5439
1.0872
1.7128




B16
LDGT2
88-95
1.11757
0.38075










0.9754
1.1094
1.4934
1.1553
1.4158
1.6429



B17
LDGT2
88-95
0.95396
0.13503










0.9673
1.1001
1.2482
1.2114
1.3643
0.9484
2.9337


B18
LDGT2
88-95
0.72512
-0.00576










0.9541
1.0331
0.9195
1.158
1.2511
1.1276
1.7748
1.3609

B19
LDGT2
88-95
0.28372
0.00704










0.9656
0.9834
0.9474
0.9412
1.0187
0.8827
1.65
0.9988
0.9646

-------

IM147 Composite Regression Coefficients, CO, 1996 and Newer Model Year LDGT2's





Regression Coefficients
Segment
Number
i
i

RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18 '
C19
P1
LDGT2
96+
1.3088
1.42622
17.1995


















P2
LDGT2
96+
1.1479
1.00342
-1.078
3.0953

















P3
LDGT2
96+
0.98949
0.98019
-2.6776
0.7119
4.3482
















P4
LDGT2
96+
0.82952
0.97186
-4.3164
0.4586
-0.0693
9.4052















P5
LDGT2
96+
0.82272
0.92541
-6.1685
0.6234
0.0838
7.9052
1.1172














P6
LDGT2
96+
0.80854
0.93687
-2.5537
0.4345
0.9927
5.0074
0.2744
5.1496













P7
LDGT2
96+
0.78612
0.93219
-0.8299
0.1902
0.867
2.2463
-0.6382
7.4747
1.8026












P8
LDGT2
96+
0.7922
0.9145
-0.5421
0.3098
1.1613
-0.1172
-0.5476
8.6914
1.4725
1.1768










-
P9
LDGT2
96+
0.79439
0.90764
0.4323
0.2804
1.57
-1.9801
-0.584
8.2734
1.281
1.4763
1.8463



. -






P10
LDGT2
96+
0.80176
' 0.89411
0.6291
0.4422
1.1925
-2.1872
-0.6255
7.6689
1.3637
1.6415
0.9728
1.2038









P11
LDGT2
96+
0.45829
0.43587
2.7438
0.9615
1.1761
-4.6902
-0.051
7.5376
0.5427
2.8889
0.9224
1.6589
0.7053








P12
LDGT2
96+
0.43361
0.27262
1.6129
0.8974
1.1808
-3.2179
0.1827
6.7307
0.6851
2.4296
1.0559
0.404
0.7027
1.0882







P13
LDGT2
96+
0.35545
0.13528
-0.1926
1.1224
-0.5669
2.979
0.8935
3.5882
0.4381
0.8157
-0.0928
0.4589
0.7161
0.668
1.9197






P14
LDGT2
96+
0.30207
0.07328
-2.5856
1.3224
-0.4318
1.9157
1.1728
1.7862
0.501
0.7493
0.097
1.2673
0.6916
0.617
1.1886
1.8756





P15
LDGT2
96+
0.11959
0.01946
0.8675
0.9676
-0.1805
1.423
0.6812
2.4687
0.9513
0.6769
0.0759
1.097
0.7126
0.6574
1.1486
0.3594
1.6893




P16
LDGT2
96+
0.09136
0.01277
-0.0995
0.9293
-0.1463
2.0115
0.7771
2.1495
0.9056
0.4482
0.4595
0.6343
0.7138
0.6364
1.0287
0.625
1.3436
1.1992



P17
LDGT2
96+
0.08571
0.00815
0.3122
0.914
0.0235
1.495
0.8462
2.28
0.6668
0.6935
0.6686
0.5081
0.7115
0.6609
0.9459
0.5995
1.3506
0.864
1.1952


P18
LDGT2
96+
0.07115
-0.00155
0.1596
0.9576
0.1434
1.207
0.8318
1.7452
0.5467
0.8902
0.2782
0.9571
0.7097
0.6328
0.8136
0.7233
1.1938
0.7903
0.6252
0.8195

P19
LDGT2
96+
0.02689
-0.00249
-0.2617
0.9243
0.5171
0.6083
0.8825
1.183
0.5554
0.926
0.5734
0.7532
0.715
0.657
0.7458
0.7682
0.6645
0.7015
0.8289
0.6613
0.7521
IM147
Phase 2 Regression Coefficients, CO, 1996 and Newer-Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19L
B11
LDGT2
96+
1.20408
1.12226










0.7609








B12
LDGT2
96+
0.62558
0.40305










 0.9685
2.1567







B13
LDGT2
96+
0.54186
0.23919










0.9716
1.5278
2.1489






B14
LDGT2
96+
0.44605
0.15037










0.9303
1.3986
1.0695
2.924





B15
LDGT2
96+
0.24307
0.03103










0.9646
1.2679
1.1889
0.9274
2.4187




B16
LDGT2
96+
0.21899
0.02112










0.9694
1.1748
0.9741
1.2313
1.9455
1.756



B17
LDGT2
96+
0.19501
0.00313










0.9536
1.2274
0.7628
1.1104
1.9911
0.8779
2.8529


B18
LDGT2
96+
0.14188
-0.00797










0.9522
1.0935
0.6831
1.2308
1.578
1.0698
0.7732
1.802

B19
LDGT2
96+
0.06636
0.01816










0.9663
0.9491
0.8971
1.0203
0.7701
0.8901
1.1191
1.0268
1.2815

-------
IM147 Composite Regression Coefficients, CO, 1981 to 1982 Model Year LDGV's
Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4 .
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
81-82
7.99839
11.8892
10.2939








i









P2
LDGV
81-82
6.58035
8.47137
3.9709
2.1482







1









P3
LDGV
81-82
5.95523
7.35499
2.3706
1.1885
4.3273
















P4
LDGV
81-82
5.84107
6.94384
0.9352
1.4
2.5147
4.4184















P5
LDGV
81-82
5.67429
6.92733
-0.9051
1.3052
1.0082
3.8467
3.2208




'I









P6
LDGV
81-82
5.60045
7.2234
-1.4742
1.4463
-0.1894
3.6997
2.3343
2.4304













P7
LDGV
81-82
5.42769
6.90375
-1.2377
1.0873
0.2076
2.5448
1.0667
3.0396
2.007












P8
LDGV
81-82
5.17703
6.82475
-1.395
0.841
-0.567
2.793
1.0784
2.1889
0.6894
3.583











P9
LDGV
81-82
5.11543
6.5602
-1.8042
0.9128
-0.049
1.6424
0.3327
1.1075
> 0.5581
3.277
3.6636










P10
LDGV
81-82
5.09166
6.36664
-1.8837
0.9302
-0.4412
1.7282
0.1522
1.3964
0.1539
3.943
1.4796
2.6588









P11
LDGV
81-82
3.36176
2.42133
-1.7143
0.9431
0.3844
-1.067
0.3333
1.7489
0.1534
1.8436
3.6102
2.5886
0.995








P12
LDGV
81-82
2.45131
1.51191
-0.6053
0.8264
0.3739
0.1848
0.3965
1.281
0.4926
1.5431
2.6906
2.8681
0.6782
1.3748







P13
LDGV
81-82
1.84222
0.81857
0.547
0.7456
0.5366
1.486
-0.1283
1.4627
0.5971
1.0752
1.2221
2.1211
0.6873
0.8834
2.0965






P14
LDGV
81-82
1.30098
0.6002
0.761
0.7639
0.4632
0.8416
-0.3448
2.0537
0.7512
1.0435
0.8919
2.1412
0.6644
0.6957
1.6479
1.2133





P15
LDGV
81-82
0.99586
0.48274
1.4122
0.6691
0.5819
1.4446
-0.1117
1.6713
0.9146
0.8677
0.8203
0.9725
0.6913
0.6885
1.0384
0.8235
1.3336




P16
LDGV
81-82
0.92334
0.37973
1.5086
0.6203
0.6059
1.5811
0.0371
1.4292
1.0143
0.8284
0.7229
0.8179
0.6963
0.6941
0.9588
0.9625
0.9091
1.8136



P17
LDGV
81-82
0.84728
0.19448
1.3677
0.6505
0.544
1.2293
0.3115
1.4583
0.9885
0.8554
0.4817
0.4999
0.7063
0.6726
0.9648
0.9625
0.9546
1.1516
2.0177


P18
LDGV
81-82
0.52289
0.04744
1.1672
0.7297
0.3484
1.2653
0.3087
1.1707
0.6643
0.8253
0.7848
0.8903
0.6988
0.7311
0.7096
0.8592
0.8362
1.0156
1.7867
0.8424

P19
LDGV
81-82
0.31963
-0.00493
1.0652
0.7221
0.5675
1.1578
0.3797
0.9834
0.7238
0.6876
0.994
0.7578
0.7005
0.7001
0.7374
0.7792
0.7058
0.244
1.232
0.646
0.9011







IM14
7 Phase 2 Regression Coefficients, CO, 1981 to 1982 Model Year LDGV's





Repression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
81-82
7.87561
8.04896









u
1.2099








B12
LDGV
81-82
4.28681
4.12849









:
0.9421
1.967







B13
LDGV
81-82
2.91132
1.85708










0.9717
1.0199
3.5026






B14
LDGV
81-82
2.35783
1.43426










0.9278
0.7999
2.9182
1.5911





815
LDGV
81-82
1.81322
0.97781









;
0.9942
0.8184
1.529
0.995
2.1475




B16
LDGV
81-82
1.64673
0.74808










1.0076
0.8215
1.3625
1.3316
1.1639
3.4285



B17
LDGV
81-82
1.35105
0.12268









!!
1.0045
0.858
1.3822
1.29
1.2026
1.7464
4.1093


B18
LDGV
81-82
0.83894
-0.09966










0.9679
0.9648
0.9202
1.1549
1.0845
1.5149
3.5169
1.2191

B19
LDGV
81-82
0.57427
-0.10409










0.9662
0.9148
0.9743
1.0447
0.9099
0.3112
2.66
0.9357
1.318

-------

IM147 Composite Regression Coefficients, CO, 1983 to 1985 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
83-85
5.65667
8.71977
10.0773


















P2
LDGV
83-85
4.39498
6.05704
2.7834
2.4038

















P3
LDGV
83-85
3.79704
5.05452
2.132
1.2291
4.6404
















P4
LDGV
83-85
3.62371
4.67806
0.5952
1.3042
2.354
5.4862















P5
LDGV
83-45
3.59484
4.60345
0.0971
1.2134
2.0391
4.9125
1.269














P6
LDGV
83-85
3.59268
4.59476
-0.3543
1.2106
2.0275
4.62
1.1146
1.0934













P7
LDGV
83-85
3.44031
4.32493
-0.5781
1.2446
0.8896
4.3893
0.6174
1.6598
1.5993












P8
LDGV
83-85
3.36343
4.2564
0.0459
1.093
0.9849
3.5699
0.5789
0.9952
1.2147
1.5455











P9
LDGV
83-85
3.33029
4.1852
-0.1659
1.1245
0.9751
3.3158
0.1104
0.6868
1.2398
1.3047
2.0264










P10
LDGV
83-85
3.29987
4.12599
0.0405
1.1476
0.8196
3.3153
-0.2824
0.8442
1.1246
1.22
1.1895
1.552









P11
LDGV
83-85
2.20291
2.25511
0.3918
0.6788
0.9189
1.616B
0.932
1.9937
0.5168
0.7853
1.9632
1.1107
0.7316








P12
LDGV
83-85
1.87972
1.58471
0.5439
0.7415
0.8536
1.6537
0.727
1.925
0.7033
0.7588
1.3654
1.0997
0.6415
1.1026







P13
LDGV
83-85
1.28242
0.91688
0.7111
0.7088
0.7627
1.8183
0.2958
1.6032
0.6711
0.6173
1.5002
0.6472
0.6692
0.8018
1.9269






P14
LDGV
83-85
1.03127
0.58797
0.6489
0.7423
0.3578
1.9308
0.4662
1.5147
0.8643
0.6344
1.5828
0.5435
0.6488
0.7632
1.5457
1.1794





P15
LDGV
83-85
0.69673
0.34058
0.7164
0.7324
0.354
1.9052
0.5776
1.1365
0.8592
0.5437
1.0426
0.8335
0.6765
0.708
0.9536
0.8489
1.4982




P16
LDGV
83-85
0.6836
0.3288
0.6397
0.7348
0.4045
1.8616
0.5991
1.1119
0.8675
0.5275
0.9881
0.8608
0.6754
0.7075
0.9317
0.828
1.269
0.931



P17
LDGV
83-85
0.61016
0.1403
0.7783
0.7181
0.5497
1.5209
0.4902
1.1225
0.8292
0.592
0.9313
0.8148
0.6905
0.7307
0.9393
0.8363
1.1503
0.6464
1.6992


P18
LDGV
83-85
0.35809
0.01542
1.0268
0.6988
0.5932
1.2395
0.5512
1.1677
0.6596
0.7122
0.8022
0.6293
0.6933
0.7668
0.6573
0.8294
1.0268
0.6176
1.4839
0.9529

P19
LDGV
83-85
0.25947
-0.01001
1.0127
0.7023
0.6463
1.1344
0.5259
1.0927
0.704
0.6265
0.7924
0.6521
0.7068
0.755
0.7028
0.7409
0.743
0.4739
1.3579
0.7294
0.7327
IM14
V7 Phase 2 Regression Coefficients, CO, 1983 to 1985 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
83-85
4.69212
5.7524










1.0243








B12
LDGV
83-85
2.83315
3.12116










0.9251
1.6564







B13
LDGV
83-85
1.86354
1.56942










0.9295
1.1338
2.8182






B14
LDGV
83-85
1.55825
1.15742










0.9063
1.0736
2.325
1.5629





B15
LDGV
83-85
1.07233
0.69467










0.943
0.9688
1.4054
1.0868
2.1751




B16
LDGV
83-85
1.05389
0.68187










0.9437
0.9647
1.3864
1.0625
1.8313
1.3471



B17
LDGV
83-85
0.89995
0.27297










0.9612
0.9964
1.3316
1.1059
1.5922
0.8992
2.8516


B18
LDGV
83-85
0.54856
0.0412










0.9415
1.0459
0.8624
1.115
1.3817
1.1042
2.1846
1.3008

B19
LDGV
83-85
0.4078
-0.005










0.9594
1.0223
0.9206
0.9952
0.9616
0.8242
1.9266
0.9809
1.0636

-------
IM147 Composite Regression Coefficients, CO, 1986 to 1989 Model Year LDGV's





Repression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
86-89
4.67926
5.71778
10.6405


















P2
LDGV
86-89
3.51709
3.81788
0.1364
2.695

















P3
LDGV
86-89
3.15842
3.11069
1.0124
1.6189
3.9459
















P4
LDGV
86-59
3.14894
3.04972
0.8613
1.649
3.2671
1.3569















P5
LDGV
86-89
3.10204
3.03267
0.7486
1.4903
2.3894
0.8299
1.8322














P6
LDGV
86-89
3.09685
3.08465
0.6252
1.4634
2.3373
0.5195
1.5731
0.9056













P7
LDGV
86-89
3.02573
2.86107
1.1098
1.2652
1.9531
0.4921
0.7359
0.8227
2.1087












P8
LDGV
86-89
2.94297
2.68329
0.7801
1.2178
1.8801
0.8138
0.7593
-0.8902
1.5027
2.0203











P9
LDGV
86-89
2.87652
2.54621
-0.0543
1.2924
1.523
1.0828
0.2369
-1.106
1.5137
1.3545
3.0417










P10
LDGV
86-89
2.74734
2.37808
-0.6515
1.4027
1.4393
1.1934
-0.1403
-0.5027
1.2073
0.9038
-0.333
4.4623









P11
LDGV
86-89
1.80566
1.04515
0.0874
0.8282
1.4645
1.1894
0.8248
-0.2838
0.6291
1.0042
1.4625
2.0759
0.8639








P12
LDGV
86-89
1.49356
0.71931
0.2804
0.7493
0.9793
1.513
0.8296
-0.0469
0.689
1.0787
0.769
1.9598
0.7434
1.2423







P13
LDGV
86-89
1.06067
0.36461
0.8207
0.6529
0.9226
1.272
0.8321
0.1223
0.6425
0.8893
0.7679
1.6076
0.7379
0.7345
2.1093






P14
LDGV
86-89
0.82231
0.26307
0.8376
0.5953
0.933
1.0658
0.8038
0.4366
0.672
0.8768
0.8378
1.4217
0.7134
0.7172
1.4718
1.4518





P15
LDGV
86-89
0.57486
0.17901
0.8519
0.6644
0.7016
1.0893
0.6865
0.5537
0.8363
0.7844
0.7334
1.1584
0.7057
0.7004
0.9553
0.9039
1.5623




P16
LDGV
86-89
0.54701
0.18097
0.946
0.6657
0.6984
0.9951
0.7165
0.6267
0.8323
0.7529
0.6652
1.0979
0.7051
0.7127
0.9675
0.8974
1.1743
1.3482



P17
LDGV
86-89
0.49266
0.13124
0.9156
0.6933
0.6436
0.974
0.7016
0.686
0.7814
0.7572
0.5724
0.9245
0.7053
0.7153
0.9399
0.8866
1.0881
0.9489
1.6484


P18
LDGV
86-89
0.29132
0.05317
0.9002
0.685
0.7018
0.9872
0.6206
0.7756
0.6789
0.7453
0.7249
0.7506
0.7065
0.7322
0.7884
0.697
0.8566
1.1084
1.3762
0.8805

P19
LDGV
86-89
0.1491
0.02273
0.8668
0.6976
0.726
0.8685
0.6806
0.7684
0.7148
0.6669
0.6743
0.7484
0.7087
0.7177
0.7096
0.7249
0.7202
0.6852
0.9434
0.7022
0.8271
IMU
17 Phase 2 Regression Coefficients, CO, 1986 to 1989 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
86-89
4.18172
3.9913










1.1684








B12
LDGV
86-89
2.21032
1.63478










1.0475
1.964







B13
LDGV
86-89
1.5326
0.82575










1.0148
1.0859
3.0948






B14
LDGV
86-89
1.21271
0.6268










0.9705
1.0461
2.17
2.0218





B15
LDGV
86-89
0.82448
0.40601










0.9653
0.9766
1.3403
1.2022
2.2818




B16
LDGV
86-89
0.77323
0.3766










0.9636
0.9888
1.3495
1.1956
1.6328
2.1301



B17
LDGV
86-89
0.67252
0.23667










0.9624
0.9661
1.2888
1.1945
1.4722
1.3414
2.4799


B18
LDGV
86-89
0.40383
0.10653










0.9566
0.984
1.0723
0.9316
1.1674
1.5464
1.9865
1.1802

B19
LDGV
86-89
0.20724
0.05209










0.9611
0.965
0.9597
0.9776
0.9801
0.9182
1.3409
0.9424
1.1303

-------
IM147 Composite Regression Coefficients, CO, 1990 to 1995 Model Year LDGV's





Repression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
90-95
5.5829
4.52493
10.561


















P2
LDGV
90-95
4.19016
2.55934
-2.4176
3.5176

















P3
LDGV
90-95
3.59909
1.7089
-2.0167
1.8381
5.8658
















P4
LDGV
90-95
3.52751
1.57279
-2.1878
1.7555
4.1493
4.0887















P5
LDGV
90-95
3.35967
1.52742
-1.8847
1.3658
3.0822
2.7595
3.0615














P6
LDGV
90-95
3.30911
1.53896
-2.1747
1.433
2.5763
2.1518
2.309
3.2307













P7
LDGV
90-95
3.20465
1.37753
-1.7351
1.2677
2.4869
1.7618
0.6153
2.8007
3.0019












P8
LDGV
90-95
3.08075
1.25584
-0.8703
1.3157
1.5808
1.0639
0.6544
1.6621
1.8801
3.4334











P9
LDGV
90-95
3.03308
1.28742
-0.9409
1.3472
1.2629
1.2814
0.1146
0.9574
2.0383
2.5679
2.6687










P10
LDGV
90-95
2.99284
1.29175
-0.6667
1.2906
1.1937
1.4946
-0.2007
0.8633
1.7797
2.4995
0.7394
2.5847









P11
LDGV
90-95
1.79334
0.39573
0.8907
0.875
0.9738
0.6525
0.6724
1.4441
0.9505
1.8704
1.0336
1.2774
0.946








P12
LDGV
90-95
1.48201
0.27031
0.8772
0.7905
0.9191
0.8699
0.6683
0.8288
0.9734
1.6847
0.9938
1.2059
0.7634
1.2265







P13
LDGV
90-95
1.22124
0.18956
0.9352
0.7049
1.1123
0.7943
0.6333
0.6174
0.819
1.3469
0.8633
0.9048
0.7518
0.8615
1.6924






P14
LDGV
90-95
0.96232
0.19909
1.2249
0.6231
0.8871
0.5243
0.6879
1.0339
0.9491
1.0285
1.2323
0.735
0.7153
0.7873
1.2566
1.1834





P15
LDGV
90-95
0.67059
0.09713
1.1865
0.6235
0.8229
0.6645
0.7023
0.9149
0.9983
0.9575
1.0487
0.5318
0.7254
0.7636
0.8921
0.7821
1.3034




P16
LDGV
90-95
0.61862
0.0845
1.1157
0.6363
0.8291
0.672
0.6393
0.9358
0.9492
0.8718
1.0229
0.6422
0.7279
0.7605
0.8704
0.8116
0.9084
1.6959



P17
LDGV
90-95
0.56789
0.04406
1.1034
0.6685
0.6934
0.7551
0.6966
0.8027
0.9189
0.7923
0.9078
0.6476
0.7299
0.7393
0.8716
0.8032
0.8822
1.1592
1.9586


P18
LDGV
90-95
0.38985
0.01218
1.0107
0.6685
0.7981
0.7109
0.6672
0.9253
0.6833
0.8064
0.9037
0.6447
0.7163
0.726
0.7487
0.7691
0.7468
1.0451
1.2236
0.9766

P19
LDGV
90-95
0.13949
0.01779
0.7784
0.7029
0.7108
0.7886
0.6798
0.8583
0.733
0.6745
0.7331
0.685
0.7139
0.7148
0.711
0.7002
0.7152
0.6362
0.9674
0.7322
0.7843
M14
\7 Phase 2 Regression Coefficients, CO, 1990 to 1995 Model Year LDGV's





Repression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
90-95
4.103
2.35754










1.4957








812
LDGV
90-95
2.32229
0.91944










1.1523
2.0251







B13
LDGV
90-95
1.76794
0.5229










1.0723
1.2322
2.7642






B14
LDGV
90-95
1.40264
0.44364










0.9979
1.1201
2.0763
1.6438





B15
LDGV
90-95
0.97281
0.25022










1.0003
1.0639
1.4071
1.0512
1.8686




B16
LDGV
90-95
0.88873
0.20771










1.0023
1.0561
1.3449
1.0929
1.2656
2.5533



B17
LDGV
90-95
0.79003
0.10796










0.9981
1.009
1.2809
1.0795
1.1947
1.6393
3.0349


B18
LDGV
90-95
0.54771
0.04247










0.9734
0.9933
1.0674
1.0348
1.0249
1.4049
2.0212
1.3189

B19
LDGV
90-95
0.19036
0.03127










0.9671
0.9711
0.9708
0.9442
0.9644
0.8618
1.3588
0.9845
1.0769

-------
IM147 Composite Regression Coefficients, CO, 1996 and Newer Model Year LDGV's
Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
96+
4.2502
1.81884
53.0314


















P2
LDGV
96+
2.79375
0.86889
-6.9133
5.3642

















P3
LDGV
96+
2.31951
0.49886
-22.4698
2.9121
7.8787
















P4
LDGV
96+
2.30066
0.48852
-20.2357
2.6582
5.7046
6.7489















P5
LDGV
96+
2.29856
0.4874
-19.0504
2.2906
4.9636
7.0228
1.4214














P6
LDGV
96+
2.17485
0.36586
-18.1249
2.1863
5.4292
5.3931
-0.6432
6.8037













P7
LDGV
96+
2.17872
0.34724
-17.6869
2.1129
5.4618
5.4355
-0.8956
6.7245
0.3983












P8
LDGV
96+
2.17729
0.32839
-15.8387
1.9759
5.2235
5.2769
-1.1683
6.4131
0.2201
1.4309











P9
LDGV
96+
2.16612
0.4024
-11.8473
1.4566
4.5337
6.4172
-1.2079
5.4459
0.3128
0.4287
3.2042










P10
LDGV
96+
2.17081
0.40258
-11.4985
1.3861
4.5521
6.4182
-1.1694
5.5108
0.2871
0.3698
2.6985
0.5987









P11
LDGV
96+
1.3457
-0.04872
-1.5411
1.3212
1.6098
6.5928
-0.785
3.2891
0.7158
-0.2278
1.3044
1.2284
1.1171








P12
LDGV
96+
0.94601
0.03677
0.1569
1.1325
0.7478
7.1762
-2.7331
2.8294
0.8581
0.5253
-0.3667
2.4476
0.8375
1.6915







P13
LDGV
96+
0.83127
0.04161
6.5433
0.7725
0.7897
2.3429
-1.2999
2.5769
0.8026
1.0385
-0.6814
0.7591
0.8719
1.3576
1.4743






P14
LDGV
96+
0.36237
0.07318
-1.7846
0.7477
1.0289
0.323
0.8116
2.2639
0.6876
0.4145
1.1104
0.4144
0.6973
0.7747
0.877
1.8748





P15
LDGV
96+
0.29315
0.05053
-0.3484
0.5009
0.8684
2.0337
0.8189
2.1406
0.8321
0.2796
-0.0113
0.3684
0.7271
0.7433
1.1116
1.0359
0.8817




P16
LDGV
96+
0.29205
0.04924
0.0274
0.4967
0.8209
2.0574
0.8019
2.0106
0.8654
0.1479
0.1851
0.3987
0.7276
0.7477
1.1209
1.0535
0.7862
0.5132



P17
LDGV
96+
0.23867
0.02554
-0.2222
0.7177
0.3166
1.7918
0.9891
1.2672
0.7007
0.7511
0.1642
0.6983
0.7129
0.7751
0.8196
1.3698
0.5666
0.1481
2.2487


P18
LDGV
96+
0.211
0.02076
1.8186
0.5321
0.4849
0.8707
1.2541
1.116
0.6247
0.9096
-0.3981
0.9301
0.7173
0.7777
0.8774
0.7931
1.0214
-0.0389
1.7607
0.6392

P19
LDGV
96+
0.05419
0.00995
0.7814
0.7041
0.7989
0.4985
0.7204
0.9434
0.6972
0.728
0.6052
0.6829
0.7065
0.7486
0.7235
0.6863
0.7334
0.6843
0.7333
0.7391
0.7203




M147 Phase 2 Regression Coefficients, CO, 1996 and Newer Model Year LDGV's





Repression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
96+
2.8132
0.6365










1.9389








B12
LDGV
96+
1.55476
0.22139










1.2687
2.444







B13
LDGV
96+
1.21697
0.12297










1.1955
1.6069
1.8026






B14
LDGV
96+
0.54556
0.13004










0.9864
1.1698
1.3748
2.3969





B15
LDGV
96+
0.4806
0.1147










0.9893
1.0617
1.3564
1.8394
0.7573




B16
LDGV
96+
0.4633
0.09077










0.985
1.0604
1.4116
1.8485
0.4981
1.6802



B17
LDGV
96+
0.33866
0.04766










0.968
1.0626
1.1629
1.9325
0.5734
0.8152
3.1735


B18
LDGV
96+
0.30627
0.03898










0.9596
1.0743
1.0207
1.4501
0.9473
0.6378
2.7247
0.6646

B19
LDGV
96+
0.07437
0.01481










0.9598
1.0096
0.9451
0.966
0.9371
1.0884
1.159
0.9807
0.9897

-------
IM147 Composite Repression Coefficients, NOx, 1981 to 1985 Model Year LDGH's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT1
81-85
1.62529
2.96654
22.8751


















P2
LDGT1
81-85
0.82038
0.84089
-0.5552
5.7627

















P3
LDGT1
81-85
0.76521
0.73117
-1.5829
4.5821
7.1986
















P4
LDGT1
81-85
0.75286
0.70493
-1.1236
4.4969
4.9474
8.0174















P5
LDGT1
81-85
0.74049
0.73769
-3.3308
4.0049
3.8497
6.663
3.702














P6
LDGT1
81-85
0.73674
0.71486
-3.9127
4.0869
3.8046
3.6966
3.0011
4.8137













P7
LDGT1
81-85
0.71739
0.69385
-3.0212
3.5234
3.5081
3.9937
-0.1792
6.3496
3.9431












P8
LDGT1
81-85
0.70652
0.67353
-2.6651
3.5435
2.1822
2.6706
0.0115
3.2904
2.8238
3.9683











P9
LDGT1
81-85
0.70505
0.67708
-1.8358
3.4819
2.4475
3.1809
0.6337
3.7784
2.952
4.1428
-2.2115










P10
LDGT1
81-85
0.70227
0.66689
-2.4177
3.494
2.3056
3.6942
0.2591
3.8357
2.4151
4.2563
-4.019
2.4819









P11
LDGT1
81-85
0.61346
0.34129
-0.06
2.6963
1.5535
2.4383
1.5201
5.655
0.7401
2.8173
-2.4441
1.0401
1.3101








P12
LDGT1
81-85
0.59022
0.30932
-0.6809
2.658
1.3697
1.2656
1.4801
3.8951
1.356
2.1592
-2.7018
1.3911
0.8585
1.6633







P13
LDGT1
81-85
0.27799
0.12998
-0.1294
0.9449
1.4979
2.9854
0.7785
0.1823
-0.313
0.9976
0.5738
0.4624
0.694
1.076
2.7355






P14
LDGT1
81-85
0.19309
0.05416
0.6065
0.856
0.9251
1.6344
0.8251
-0.094
0.4201
1.4703
0.998
0.7742
0.5699
0.79
1.6886
1.7582





P15
LDGT1
81-85
0.10887
0.02502
0.5161
0.6742
1.1768
1.4558
0.2902
1.3526
0.7996
0.8754
0.732
0.6248
0.6617
0.8183
0.8141
1.2315
1.3505




P16
LDGT1
81-85
0.09725
0.01363
0.4935
0.6784
1.1208
1.1307
0.3923
0.9511
0.8969
0.5086
0.7311
0.6773
0.699
0.7813
0.8355
1.1375
1.1283
1.0972



P17
LDGT1
81-85
0.09344
0.01174
0.5252
0.7346
0.9639
1.1015
0.6838
0.7005
0.7356
0.6406
0.6359
0.4841
0.6953
0.7762
0.8511
1.1597
1.0383
1.0222
1.0523


P18
LDGT1
81-85
0.05721
0.00436
0.6442
0.6817
1.0499
1.0725
0.6056
1.15
0.5722
0.7474
0.8465
0.5131
0.6842
0.8341
0.6429
0.922
0.8319
1.1396
0.7657
0.8484

P19
LDGT1
81-85
0.0165
0.00076
0.7099
0.6986
0.7653
0.7747
0.7168
0.9007
0.7285
0.7007
0.7342
0.6359
0.7093
0.736
0.7337
0.6964
0.7033
0.7238
0.7471
0.7212
0.7201
IM147 Phase 2 Regression Coefficients, NOx, 1981 to 1985 Model Year LDGH's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT1
81-85
1.076
0.85959










3.4954








B12
LDGT1
81-85
1.00048
0.67351










2.4254
3.4463







B13
LDGT1
81-85
0.38989
0.20314










0.8889
1.8075
3.7573






B14
LDGT1
81-85
0.27037
0.0891










0.8213
1.2937
2.454
2.3704





B15
LDGT1
81-85
0.15297
0.04337










0.8745
1.292
1.0958
1.7178
1.8359




B16
LDGT1
81-85
0.13123
0.01886










0.9513
1.0861
1.1153
1.5763
1.4991
1.5837



B17
LDGT1
81-85
0.12525
0.01412










0.9423
1.0698
1.128
1.6173
1.4024
1.4526
1.3599


B18
LDGT1
81-85
0.08127
0.0094










0.8966
1.2134
0.8469
1.3163
1.0875
1.7522
1.1639
1.0764

B19
LDGT1
81-85
0.01759
0.00132










0.9552
1.0089
0.9919
0.9568
0.9493
1.0003
1.0401
0.9479
1

-------
1
M147 Composite Regression Coefficients, NOx, 1986 to 1989 Model Year LDGH's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT1
86-89
1.23043
2.06694
23.5015


















P2
LDGT1
86-89
0.70187
0.74907
1.2554
4.6182

















P3
LDGT1
86-89
0.65788
0.66459
1.9511
3.4741
6.6849
















P4
LDGT1
86-89
0.65847
0.6622
2.0121
3.4718
6.5638
0.4801















P5
LDGT1
86-69
0.60304
0.65314
0.8979
2.5888
3.7778
0.693
7.82














P6
LDGT1
86-89
0.60221
0.64909
0.8986
2.5939
3.729
-0.3487
7.391
2.7912













P7
LDGT1
86-89
0.58554
0.63588
-0.2658
2.2631
2.7626
-0.2519
4.6311
4.814
3.3278












P8
LDGT1
86-89
0.57068
0.60583
0.1372
2.1042
2.0549
-1.463
4.181
1.0084
2.9915
4.4107











P9
LDGT1
86-89
0.57122
0.60597
0.1342
2.1057
2.0299
-1.4503
4.1619
0.9698
2.989
4.3952
0.1053










P10
LDGT1
86-89
0.56454
0.59282
-0.9316
2.1076
1.5254
-0.8557
3.4898
1.6834
2.3002
4.575
-1.5419
3.1754









P11
LDGT1
86-89
0.44918
0.31619
0.8774
1.547
0.3721
-1.2258
3.7047
1.8503
0.299
3.4571
-0.7772
1.6757
1.4183








P12
LDGT1
86-89
0.39015
0.23116
2.1899
1.3771
-0.3977
-1.386
2.7841
0.6668
0.7794
1.8436
-0.1039
1.861
0.955
2.7651







P13
LDGT1
86-89
0.21107
0.11066
-0.9618
0.6673
1.0909
0.613
1.2202
0.9175
0.3457
1.1499
1.3016
-0.1198
0.8075
1.3691
2.4303






P14
LDGT1
86-89
0.15824
0.06623
-0.4774
0.6974
0.7145
0.8449
1.214
0.9814
0.6256
0.7295
1.5692
0.6474
- 0.6399
0.8685
1.8063
1.4835





P15
LDGT1
86-89
0.10582
0.03533
0.0217
0.6721
0.9237
 0.9185
0.462
1.2051
0.9519
0.6397
1.0663
0.5564
0.7033
0.8047
0.8872
1.1532
1.2727




P16
LDGT1
86-89
0.09624
0.03427
0.4994
0.6974
0.6351
1.2145
0.5601
0.5254
1.017
0.3771
0.9662
0.7977
*0.7267
0.8275
0.9401
1.0394
0.9564
1.2214



P17
LDGT1
86-89
0.09019
0.02973
0.3688
0.7491
0.5027
1.1374
0.7202
0.7519
0.9628
0.4449
0.9374
0.4114
i 0.7103
0.847
0.8964
1.087
0.896
1.1054
1.2517


P18
LDGT1
86-89
0.05194
0.01155
0.561
0.6874
0.7247
0.8944
0.4555
1.1866
0.7413
1.03
0.6717
0.4644
0.7014
0.8099
0.637
0.8736
0.8444
0.9947
0.7536
0.9298

P19
LDGT1
86-89
0.01448
0.0007
0.522
0.7051
0.7802
0.7612
0.7043
0.8674
0.7473
0.7107
0.6734
0.7097
10.7081
0.7257
0.7131
0.7176
0.7067
0.7115
0.6881
0.7158
0.7428
IM147 Phase 2 Regression Coefficients, NOx, 1986 to 1989 Model Year LDGH's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT1
86-89
0.71447
0.56214










2.9488








B12
LDGT1
86-89
0.58562
0.33666










1.7796
' 4.5068







B13
LDGT1
86-69
0.29253
0.15935










1.0449
2.1599
3.1651






B14
LDGT1
86-89
0.22004
0.08924










0.9001
1.3657
2.486
1.9841





B15
LDGT1
86-89
0.14504
0.05










0.9776
1.164
1.1562
1.5604
1.7468




B16
LDGT1
86-89
0.13142
0.04594










' 1.0201
1.0708
1.2975
1.3785
1.3303
1.5302



B17
LDGT1
86-69
0.12255
0.04125










0.977
1.1131
1.218
1.4703
1.2356
1.4639
1.602


B18
LDGT1
86-89
0.0715
0.02293










0.937
1.1335
0.8403
1.2196
1.0929
1.5766
0.9306
1.2015

B19
LDGT1
86-89
0.01559
0.00168










0.9648
0.9858
0.9551
0.9715
0.9562
1.0147
0.9809
0.9807
0.9956

-------
IM147 Composite Regression Coefficients, NOx, 1990 to 1995 Model Year LDGTVs





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT1
90-95
1.15989
1.24128
32.9634


















P2
LDGT1
90-95
0.72181
0.40146
0.679
4.5914

















P3
LDGT1
90-95
0.69216
0.32475
2.1542
3.4912
6.7253
















P4
LDGT1
90-95
0.68749
0.3123
2.1371
3.4737
5.023
5.8563















P5
LDGT1
90-95
0.62455
0.33205
3.4043
2.3634
1.7239
5.0562
8.7446














P6
LDGT1
90-95
0.61978
0.33046
2.2722
2.3005
1.5599
2.9584
8.2913
6.1514













P7
LDGT1
90-95
0.58792
0.29749
2.0622
1.8473
0.0555
3.6238
4.2818
7.9809
5.3164












P8
LDGT1
90-95
0.55746
0.26129
5.9179
1.7445
-0.5662
1.235
4.3563
0.782
3.7446
7.1976











P9
LDGT1
90-95
0.54008
0.24653
4.1085
1.8313
-1.1591
2.0187
2.9278
-0.7348
3.4121
5.4986
7.8688










P10
LDGT1
90-95
0.53603
0.2342
2.929
1.8441
-1.4993
2.6412
2.6552
0.0076
2.7177
5.458
6.0386
2.7176









P11
LDGT1
90-95
0.45579
0.05778
3.2806
1.051
-0.2096
0.7143
2.8133
1.4291
0.9583
2.9071
6.0179
0.9428
1.4469








P12
LDGT1
90-95
0.36036
0.0273
4.3485
0.9563
-0.635
-0.3902
2.43
0.4675
1.7444
0.5941
4.1616
1.3325
0.6814
3.9706







P13
LOGT1
90-95
0.17758
0.04529
0.6553
0.7331
0.4798
1.1424
1.2661
0.9419
0.3362
0.8
1.2658
0.277
0.7426
1.2309
2.6602






P14
LDGT1
90-95
0.12741
0.02345
0.6061
0.7033
0.6424
1.2708
1.124
1.4838
0.5682
0.7251
0.9404
0.6888
0.6431
0.8253
1.6054
1.8253





P15
LDGT1
90-95
0.07822
0.01761
1.1782
0.6831
0.7996
1.3552
0.7806
0.9482
0.6863
0.8429
1.0081
0.3774
0.6949
0.7428
0.8865
1.0954
1.3651




P16
LDGT1
90-95
0.06969
0.01628
1.367
0.7128
0.824
1.0691
0.8406
0.6506
0.711
0.7051
0.914
0.6359
0.7085
0.724
0.8452
1.0797
1.0435
1.2099



P17
LDGT1
90-95
0.06569
0.00712
1.0171
0.713
0.7449
0.9912
0.8383
0.5662
0.7047
0.7856
0.8682
0.5184
0.7126
0.7606
0.8503
1.0453
1.0145
1.1224
1.218


P18
LDGT1
90-95
0.03909
0.0008
0.7599
0.7108
0.6532
1.2612
0.7148
1.122
0.5737
0.837
0.7408
0.6827
0.6897
0.8094
0.6826
0.8525
0.8302
1.1087
0.8134
0.8578

P19
LDGT1
90-95
0.01086
0.00079
0.757
0.7
0.7412
0.7991
0.7237
0.854
0.7206
0.6726
0.7413
0.7075
0.7135
0.7264
0.7129
0.729
0.7202
0.7122
0.7434
0.6941
0.73
IM147 Phase 2 Regression Coefficients, NOx, 1990 to 1995 Model Year LDGH's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT1
90-95
0.70725
0.11508










3.158








B12
LDGT1
90-95
0.54083
0.02981










1.5474
5.8806







B13
LDGT1
90-95
0.24246
0.06072










0.9747
1.7935
3.6






B14
LDGT1
90-95
0.1753
0.03555










0.8927
1.2292
2.2351
2.4522





B15
LDGT1
90-95
0.10866
0.02903










0.9331
1.1278
1.1969
1.4839
1.8483




B16
LDGT1
90-95
0.09571
0.02747










0.9823
1.0076
1.1844
1.4428
1.4242
1.6458



B17
LDGT1
90-95
0.08918
0.01127










0.9705
1.0641
1.1573
1.415
1.3597
1.5734
1.7341


B18
LDGT1
90-95
0.05491
0.00454










0.9276
1.1839
0.9094
1.1571
1.0953
1.6597
1.2012
1.1201

B19
LDGT1
90-95
0.01259
0.0022










0.9623
0.9896
0.9651
0.9817
0.9732
0.9769
1.063
0.9406
0.9926

-------
IM147 Composite Regression Coefficients, NOx, 1996 and Newer Model Year LDGH's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
I .
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT1
96+
0.75598
0.48362
23.6975


















P2
LDGT1
96+
0.46673
0.18401
12.1233
3.0933

















P3
LDGT1
96+
0.46374
0.16288
12.749
2.6461
3.1462









*






P4
LDGT1
96+
0.46514
0.16338
12.1028
2.534
2.3048
4.9422















P5
LDGT1
96+
0.3791
0.14765
18.1804
1.5554
-1.8687
0.8812
12.6955





:








P6
LDGT1
96+
0.38121
0.14804
18.193
1.5564
-1.9029
1.0072
12.8058
-0.5274




:








P7
LDGT1
96+
0.3284
0.12947
19.8606
1.0854
-0.0898
-8.9504
5.5367
-2.3077
9.8773












P8
LDGT1
96+
0.31982
0.14076
18.8826
0.9749
1.2061
-5.4908
2.7593
-4.5733
4.9464
8.5778











P9
LDGT1
96+
0.32062
0.15138
18.5988
0.8971
1.8707
-4.2693
2.4773
-3.9369
4.9682
9.0399
-2.7492










P10
LDGT1
96+
0.24391
0.13494
9.3292
0.7629
2.6156
-11.0979
1.3803
2.9882
1.7874
11.2329
-6.1993
8.6929









P11
LDGT1
96+
0.18099
0.10785
0.7068
0.5528
3.4731
-8.9995
-0.2732
4.7615
0.0782
7.2458
-2.3664
2.4504
! 1.28








P12
LDGT1
96+
0.17159
0.09436
-0.6219
0.5365
2.5353
-3.0512
-0.8472
1.9087
-0.1596
6.9805
-2.7139
3.1761
s 0.9818
1.8863







P13
LDGT1
96+
0.12899
0.07087
1.7268
0.3591
2.536
1.5628
0.5764
-2.7771
1.4626
2.9783
-1.5186
1.4469
0.7662
1.0775
1.7081






P14
LDGT1
96+
0.10789
0.06233
-1.0403
0.4537
1.9505
0.0036
0.7498
1.6955
0.3857
3.6594
-0.8929
0.9085
,0.7212
0.4779
1.0256
1.5265





P15
LDGT1
96+
0.06499
0.02733
-0.039
0.6102
1.5778
0.9818
0.0528
0.1089
1.1186
2.0576
-0.9366
1.0631
H 0.7396
0.86
0.6974
0.7671
1.3416




P16
LDGT1
96+
0.05153
0.02183
0.4719
0.5741
2.1518
-0.4167
0.1408
-0.69
0.7472
2.1867
0.0185
0.9315
10.8149
0.7897
0.797
0.6681
0.6861
2.2482



P17
LDGT1
96+
0.04794
0.01483
0.175
0.6024
1.5796
0.5006
0.714
-1.1578
0.8449
2.0981
-0.6903
1.1098
r0.7149
0.9709
0.8288
0.7074
0.7195
1.7387
1.6876


P18
LDGT1
96+
0.02782
0.00211
0.6747
0.6416
1.5148
-0.1218
0.5752
0.4924
0.7534
1.8426
-0.2848
0.9695
i! 0.7007
0.83
0.7552
0.7376
0.7664
1.1279
0.8032
0.758

P19
LDGT1
96+
0.00403
0.00098
0.7432
0.7168
0.7163
0.6233
0.7292
1.0286
0.6325
0.9521
0.6402
0.7217
'0.7125
0.7112
0.7066
0.7343
0.6734
0.7676
0.7274
0.7239
0.7481
IM147
Phase 2 Regression Coefficients, NOx, 1996 and Newer Model Year LDGH's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
l;
4
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT1
96+
0.28558
0.12907










H2.2171








B12
LDGT1
96+
0.24779
0.09313










11.6406
3.4138







B13
LDGT1
96+
0.18123
0.08816










: 1.252
1.6471
2.2975






B14
LDGT1
96+
0.15739
0.06574










j! 0.9727
1.3922
1.7725
1.7148




. - 
B15
LDGT1
96+
0.09057
0.02458










,1.0721
1.1839
0.9796
1.028
1.925




B16
LDGT1
96+
0.07613
0.02379










11.1061
1.2115
1.0191
0.9869
1.1519
2.6649



B17
LDGT1
96+
0.07139
0.00843










1.0352
1.3901
1.1173
0.956
1.1159
2.3796
2.0104


B18
LDGT1
96+
0.04333
-0.00582










; 0.9841
1.2998
1.0418
0.9729
1.1585
1.4094
0.9684
1.1153

B19
LDGT1
96+
0.00565
0.00178










0.9761
0.9749
0.9793
0.9737
0.9206
1.0687
0.9432
0.9717
1.0089

-------
IM147 Composite Regression Coefficients, NOx, 1981 to 1985 Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT2
81-85
1.99804
3.69101
10.2569


















P2
LDGT2
81-85
0.87409
0.91821
-7.9377
6.5305

















P3
LDGT2
81-A5
0.84122
0.83444
-8.613
5.4145
7.2247
















P4
LDGT2
81-85
0.82892
0.89342
-10.2897
5.4633
2.5096
8.8657















P5
LDGT2
81-85
0.80333
0.89081
-9.4068
4.7319
0.276
9.1558
5.3418














P6
LDGT2
81-85
0.7983
0.87735
-13.437
4.8675
-0.2244
7.2171
4.8373
4.2644













P7
LDGT2
81-85
0.79972
0.89425
-13.7206
4.6923
-0.887
7.62
4.3577
4.6628
1.0456












P8
LDGT2
81-85
0.79473
0.84415
-11.4499
4.5789
-1.4261
5.7988
3.4916
2.7174
1.1653
3.2995











P9
LDGT2
81-85
0.79705
0.83069
-11.6615
4.6051
-1.4956
5.8933
3.2922
2.8161
1.1255
3.028
0.9261










P10
LDGT2
81-85
0.79648
0.81111
-11.8131
4.561
-1.2986
5.9935
2.4713
3.4666
0.516
3.1827
-0.4726
2.7254









P11
LDGT2
81-85
0.6659
0.51142
-7.4827
3.0898
-1.5249
6.9352
3.5295
2.9385
1.4316
3.7346
1.3238
-2.5498
1.2169








P12
LDGT2
81-85
0.54957
0.33422
-8.1307
2.4318
-0.7549
0.1755
1.141
5.7388
3.1487
2.2295
0.7669
0.0157
0.6425
3.1201







P13
LDGT2
81-85
0.26368
0.1926
-3.0729
0.495
1.2524
2.9861
-0.2928
2.9259
1.4715
0.5835
2.0376
-0.4876
0.712
1.7639
2.5466






P14
LDGT2
81-85
0.17079
0.09831
-1.9366
0.7267
0.5707
1.4814
-0.6512
2.8236
1.4133
1.5048
1.8051
0.7881
0.6265
1.0991
1.5732
1.6465





P15
LDGT2
81-85
0.13538
0.05816
-1.2593
0.6843
0.9631
1.699
-1.1762
2.6604
1.5116
1.2555
1.0619
1.1308
0.6644
0.9993
0.9615
1.3864
0.9139




P16
LDGT2
81-85
0.12927
0.04855
-0.6587
0.6411
0.9878
1.2005
-0.6445
2.468
1.4956
0.99
1.0154
1.2544
0.6922
0.8896
1.0162
1.349
0.717
0.6881



P17
LDGT2
81-85
0.12798
0.04485
-0.3609
0.6627
0.916
1.0272
-0.4442
2.2519
1.3213
1.1276
1.0486
1.1459
0.6823
0.8485
0.9897
1.3779
0.7091
0.682
0.6099


P18
LDGT2
81-85
0.08606
0.02155
0.3328
0.587
1.0724
1.0072
0.5742
1.9792
0.4674
1.4066
1.3569
-0.0211
0.6682
0.8649
0.7604
0.9339
0.6473
0.8806
1.1799
0.9731

P19
LDGT2
81-85
0.02488
0.0013
1.1429
0.6401
0.9555
0.8446
0.5989
0.8719
0.8311
0.6923
0.5888
0.7318
0.7118
0.6787
0.7213
0.7279
0.7365
0.6649
0.7072
0.679
0.7799
IM147 Phase 2 Repression Coefficients, NOx, 1981 to 1985 Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT2
81-85
1.42447
1.55851










3.2137








B12
LDGT2
81-85
1.03629
0.77984










1.5673
6.4951







B13
LDGT2
81-85
0.38433
0.27378










0.8325
2.718
3.5162






B14
LDGT2
81-85
0.26921
0.18815










0.8123
1.6716
2.4085
2.1998





B15
LDGT2
81-85
0.22469
0.11816










0.8718
1.4697
1.4685
1.8766
1.2618




B16
LDGT2
81-85
0.19939
0.09578










0.9679
1.128
1.6012
1.7461
0.8574
1.4532



817
LDGT2
81-85
0.19039
0.09229










0.9221
1.0819
1.4847
1.7994
0.8974
1.351
1.5644


B18
LDGT2
81-85
0.15301
0.07095










0.8347
1.2468
1.0994
1.3645
0.8676
1.6334
1.4952
1.0081

B19
LDGT2
81-85
0.03621
0.00936










0.943
0.9237
0.9993
0.9988
0.978
0.8541
1.0342
0.8947
1.1014

-------
IM147 Composite Regression Coefficients, NOx, 1986 to 1987 Model Year LDGT2's
Regression Coefficients
Segment.
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT2
86-87
1.61179
3.13663
31.3453


















P2
LDGT2
86-87
0.97795
1.10882
-3.0631
5.8632

















P3
LDGT2
86-87
0.94502
0.9839
-5.0759
4.548
8.9192
















P4
LDGT2
86-87
0.93501
0.93053
-3.3825
4.4591
5.2516
12.419






.








P5
LDGT2
86-87'
0.85394
0.96285
-4.4203
3.6091
-1.7208
11.4179
9.1873














P6
LDGT2
86-87
0.84757
0.91259
-4.4095
3.6676
-1.5513
6.2687
8.3678
9.6507













P7
LDGT2
86-87
0.85029
0.91803
-4.0853
3.616
-2.1286
6.0176
7.8929
10.0878
0.7099












P8
LDGT2
86-87
0.85275
0.92194
-3.8044
3.5415
-2.4148
4.8872
7.7612
9.5053
0.6261
1.3726











P9
LDGT2
86-87
0.83178
0.86005
-3.778
4.3986
-6.3555
9.9437
4.6162
8.3808
-0.6741
-0.2851
9.9702










P10
LDGT2
86-87
0.80985
0.90764
-7.0196
4.1229
-5.9089
8.2497
2.6936
10.9511
-1.1815
0.3123
3.2549
6.8821









P11
LDGT2
86-87
0.76148
0.62189
-8.9566
3.094
-4.9268
6.2947
5.15
9.9243
-2.4765
-0.213
3.9969
6.4604
0.8842








P12
LDGT2
86-87
0.59003
0.3735
-2.4217
2.087
-2.6617
4.0616
2.224
1.9821
-1.3263
-2
4.7639
5.4191
,0.2795
5.406



.



P13
LDGT2
86-87
0.33385
0.19007
0.2814
1.1386
0.5118
3.255
-0.1077
2.4058
0.5387
-1.7814
4.2622
-0.1798
0.5687
0.4608
3.2021






P14
LDGT2
86-87
0.20099
0.13811
1.8759
0.6935
0.5775
3.0589
0.9302
1.5194
0.7032
-0.6974
2.2407
0.0121
0.4793
0.2025
1.9049
2.1646





P15
LDGT2
86-87
0.1038
0.0461
0.3765
0.6236
1.678
0.8805
1.0648
1.097
0.2424
-0.0264
1.7158
0.0601
0.6449
0.9194
1.0397
1.1878
1.3178




P16
LDGT2
86-87
0.09657
0.03819
0.6854
0.5604
1.7082
0.6146
0.6222
1.3221
0.5089
-0.0753
1.3097
0.8115
0.675
0.9002
0.9782
1.1823
1.0924
0.8604



P17
LDGT2
86-87
0.08202
0.02243
0.7341
0.6449
1.3141
-0.1109
0.6071
1.3461
0.4166
0.5635
1.6121
-0.0273
0.695
0.8997
0.9541
1.1481
1.0157
0.8336
1.7925


P18
LDGT2
86-87
0.05719
0.02206
0.9248
0.6705
1.2239
-0.0494
0.6674
1.7312
0.3068
0.8156
1.3908
0.0199
ii 0.6845
0.9323
0.8095
0.9223
0.7808
0.9324
1.241
0.7403

P19
LDGT2
86-87
0.01482
0.00786
0.6527
0.6745
0.6642
0.6799
0.7944
0.7706
0.6813
0.8969
0.6883
0.6733
0.71
0.7009
0.7385
0.7569
0.6679
0.7207
0.8026
0.7243
0.7208









IM147 Phase 2 Regression Coefficients, NOx, 1986 to 1987 Model Year LDGT2's





Regress
on Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
CI
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT2
86-87
1.44862
1.6677










2.507








B12
LDGT2
86-87
0.92821
0.61792










. 0.4543
9.8299







B13
LDGT2
86-87
0.45405
0.32299










0.7862
0.5919
4.3588






B14
LDGT2
86-87
0.27745
0.22123










0.6178
0.2412
2.551
2.9603





B15
LDGT2
86-87
0.14699
0.0967










. 0.8032
1.2698
1.2876
1.6105
1.8179




B16
LDGT2
86-87
0.1382
0.07565










0.859
1.1449
1.2732
1.5831
1.6009
0.9193



B17
LDGT2
86-87
0.1172
0.05283










0.8945
1.2281
1.135
1.5637
1.3545
1.3493
2.1364


B18
LDGT2
86-87
0.08824
0.05293










0.8887
1.3239
0.9624
1.2543
1.0381
1.5212
1.2998
0.9849

B19
LDGT2
86-87
0.0176
0.00876










0.958
0.9848
0.9632
0.9799
0.9647
0.9458
1.1217
0.9723
0.9846

-------
IM147 Composite Regression Coefficients, NOx, 1988 to 1995 Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10-
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT2
88-95
1.24878
2.35108
23.5648


















P2
LDGT2
88-95
0.82176
0.87411
0.1479
4.6355

















P3
LDGT2
88-95
0.77845
0.71657
-1.7162
3.425
8.1364
















P4
LDGT2
88-95
0.7742
0.70093
-1.8478
3.4367
6.2476
5.1308















P5
LDGT2
88-95
0.73556
0.71294
-1.4757
2.6692
3.7613
3.2899
6.2663














P6
LDGT2
88-95
0.72738
0.72398
-2.5889
2.6139
3.8661
-0.5835
5.6306
6.8957













P7
LDGT2
88-95
0.70212
0.63963
-1.6939
2.0359
3.1437
0.5064
1.7183
8.4446
5.0667












P8
LDGT2
88-95
0.68022
0.60531
-0.4848
1.8702
2.347
-1.174
1.1716
2.9882
4.0341
6.3227











P9
LDGT2
88-95
0.67704
0.59719
-1.6152
1.9533
1.922
-0.993
0.5073
2.3082
3.7294
5.9607
3.1202










P10 .
LDGT2
88-95
0.66525
0.59249
-1.2143
1.9942
1.1629
0.3777
-0.1791
2.4339
2.4689
6.3272
-0.1222
4.5356









P11
LDGT2
88-95
0.49566
0.16456
-1.995
' 0.9652
1.6003
1.6143
0.2178
0.8508
-0.2399
3.1105
1.8601
2.7668
1.9044








P12
LDGT2
88-95
0.39011
0.13915
-0.5048
0.8942
0.6646
0.7011
0.5649
-0.3036
0.9763
0.3149
0.4631
3.3575
0.9895
3.9381







P13
LDGT2
88-95
0.22071
0.13586
0.1295
0.5892
1.2326
1.6722
0.4435
0.7176
0.4615
0.651
1.5772
0.5428
0.7152
1.6198
2.5117






P14
LDGT2
88-95
0.16814
0.08085
0.4177
0.6242
1.2076
1.54
0.4306
1.0374
0.8469
0.7753
1.5915
0.5229
0.5567
1.0483
1.735
1.6545





P15
LDGT2
88-95
0.09792
0.04274
1.2914
0.6299
0.932
1.5301
0.7496
0.6925
0.7213
0.5876
1.3142
0.4284
0.6686
0.9379
0.8221
1.0317
1.4681




P16
LDGT2
88-95
0.08515
0.04
1.3815
0.6274
0.8992
1.2443
0.777
0.5902
0.9112
0.3659
1.1635
0.6339
0.6996
0.8562
0.8431
0.9855
1.1161
1.2384



P17
LDGT2
88-95
0.0793
0.03
1.1604
0.6609
0.7332
1.166
0.8211
0.5607
0.8664
0.4784
1.1288
0.4527
0.7155
0.8591
0.8406
0.9915
1.0422
1.1105
1.2887


P18
LDGT2
88-95
0.04482
0.00528
1.0205
0.6891
0.8472
0.9762
0.6666
1.0589
0.5961
0.8411
0.7658
0.5158
0.703
0.8891
0.6603
0.8286
0.8216
1.0348
0.9346
0.8792

P19
LDGT2
88-95
0.0136
-0.00252
0.7213
0.711
0.747
0.8258
0.7089
0.7055
0.696
0.744
0.7329
0.7233
0.715
0.7382
0.7138
0.716
0.7117
0.7363
0.7333
0.6995
0.7243
IM147 Phase 2 Regression Coefficients, NOx, 1988 to 1995 Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT2
88-95
0.71642
0.34014










3.1858








B12
LDGT2
88-95
0.55127
0.22451










1.6945
5.1988







B13
LDGT2
88-95
0.30245
0.20374










0.8854
2.4317
3.3283






B14
LDGT2
88-95
0.23377
0.14045










0.7529
1.6901
2.356
2.1936





B15
LDGT2
88-95
0.13683
0.07379










0.881
1.4099
1.0647
1.3754
2.0165




B16
LDGT2
88-95
0.11912
0.06757










0.9498
1.1559
1.1515
1.31
1.5401
1.6409



B17
LDGT2
88-95
0.10918
0.04812










0.9645
1.1668
1.1296
1.3343
1.4075
1.494
1.8722


B18
LDGT2
88-95
0.0635
0.01509










0.9291
1.3099
0.8518
1.1259
1.093
1.5612
1.2713
1.1432

B19
LDGT2
88-95
0.01423
0.0016










0.9672
1.0052
0.9647
0.96
0.9599
1.0009
1.0227
0.9483
1.0006

-------
IM147 Composite Regression Coefficients, NOx, 1996 and Newer Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGT2
96+
0.578
0.71379
15.4654


















P2
LDGT2
96+
0.33821
0.37061
-21.218
3.256

















P3
LDGT2
96+
0.33307
0.33021
-17.4893
2.5344
3.8906
















P4
LDGT2
96+
0.30416
0.30038
-14.5682
2.2242
11.6872
-12.2279















P5
LDGT2
96+
0.29072
0.28842
-17.6088
1.8185
9.3908
-10.8536
4.734














P6
LDGT2
96+
0.2883
0.30337
-11.6965
1.745
9.8903
-10.7171
5.8457
-7.1022













P7
LDGT2
96+
0.24718
0.25136
-20.0849
1.7221
5.3396
-2.4287
-0.5675
-9.9875
8.4455












P8
LDGT2
96+
0.23492
0.2519
-22.4763
1.5623
5.2794
-3.3995
-2.4191
-8.9711
6.8514
7.9536











P9
LDGT2
96+
0.23608
0.24316
-22.27
1.5055
5.3167
-3.4781
-3.0309
-9.6937
6.8514
8.1317
3.1882










P10
LDGT2
96+
0.2359
0.23077
-20.9734
1.5584
5.6716
-3.4051
-4.2894
-8.7832
6.8235
6.3126
1:8606
3.9221









P11
LDGT2
96+
0.18371
0.16444
-10.5542
0.5675
3.1636
-1.1457
-3.3023
0.5825
3.4287
3.1947
4.4137
2.9738
1.3085








P12
LDGT2
96+
0.18578
0.16354
-9.9999
0.6169
2.8048
-0.7267
-3.2312
0.1935
3.5982
3.0439
4.2462
2.9303
1.2191
0.2662







P13
LDGT2
96+
0.12127
0.05817
-4.2485
0.6116
4.4173
-3.0746
-1.9566
-5.1095
2.1593
3.236
5.8951
2.3221
1.0537
-0.1452
1.6328






P14
LDGT2
96+
0.06953
0.0183
4.3668
0.7373
2.7372
0.575
-1.3744
-3.22
2.844
-0.5548
1.4212
3.1253
0.6404
1.0543
0.791
1.4102





P15
LDGT2
96+
0.05854
0.01128
2.8175
0.7837
1.5804
1.7698
-0.6221
-2.7325
2.4484
0.2815
-0.0086
2.3246
0.5592
1.0307
0.7388
1.1608
0.7986




P16
LDGT2
96+
0.05584
0.00873
1.9819
0.7799
1.6432
1.5678
-0.4135
-2.4395
2.0557
0.0994
0.2982
1.9349
0.6182
0.9336
0.7441
1.0662
0.7148
1.3475



P17
LDGT2
96+
0.05251
0.00847
0.351
0.7816
1.4739
1.5467
-0.3431
-2.0093
2.0146
-0.208
0.4812
1.4451
0.723
0.6611
0.7379
1.0331
0.6536
1.3684
2.1742


P18
LDGT2
96+
0.01918
0.00064
1.8364
0.7759
1.0399
1.1145
0.2474
-0.0511
0.6696
0.6814
0.6723
1.4583
0.5956
0.9049
0.7647
0.8482
0.6884
1.3503
-0.5973
0.8893

P19
LDGT2
96+
0.00325
0.00226
0.7097
0.7313
0,8044
0.6612
0.6632
0.833
0.6804
0.6611
0.5647
0.8016
0.72
0.6732
0.7118
0.7105
0.7272
0.7857
0.7169
0.7106
0.6983
IM147
3hase 2 Regression Coefficients, NOx, 1996 and Newer Model Year LDGT2's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGT2
96+
0.25624
0.28161










2.2649








B12
LDGT2
96+
0.25427
0.26879










1.8738
1.3389







B13
LDGT2
96+
0.18429
0.10134










1.6678
0.8038
2.0982






B14
LDGT2
96+
0.11213
0.05754










1.2535
1.3129
1.0561
1.9394





B15
LDGT2
96+
0.08972
0.02934










1.1523
0.9849
1.0364
1.4467
1.3152




B16
LDGT2
96+
0.08172
0.02161










1.1412
0.9687
1.0064
1.2969
1.0936
2.5063



B17
LDGT2
96+
0.07624
0.01744










1.1996
0.6743
0.9948
1.295
1.0153
2.0425
3.1694


B18
LDGT2
96+
0.03345
0.0095










0.929
1.0616
1.0355
1.1034
0.9077
1.821
-0.1023
1.2122

B19
LDGT2
96+
0.00415
0.0038










0.9821
0.9175
0.9698
0.9467
0.9808
1.0103
1.1121
0.9437
0.9983

-------
IM147 Composite Regression Coefficients, NOx, 1981 to 1982 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
81-82
0.91799
1.65041
42.7692


















P2
LDGV
81-82
0.63601
0.72708
17.5251
4.1787

















P3
LDGV
81-82
0.52331
0.58513
-4.6789
2.4424
11.7817
















P4
LDGV
81-82
0.52237
0.58386
-6.215
2.4752
10.4671
3.1134















P5
LDGV
81-82
0.50775
0.50405
-4.2723
2.2111
9.2772
2.5761
3.9991














P6
LDGV
81-82
0.50352
0.48347
-3.9075
2.1456
8.9952
1.1125
3.4211
5.3989













P7
LDGV
81-82
0.49839
0.48687
-4.3121
2.0136
8.6708
0.9449
1.0442
5.8614
2.4035












P8
LDGV
81-82
0.46669
0.46683
1.6928
1.6175
7.9174
-2.5315
1.6902
0.2702
0.6553
7.5772











P9
LDGV
81-82
0.46311
0.452
-0.8119
1.7211
7.5725
-2.1824
0.4138
-2.0068
0.7582
7.4243
3.9487










P10
LDGV
81-82
0.46376
0.44782
-1.0048
1.7433
7.4758
-2.0726
0.0943
-1.7963
0.4584
7.4711
3.3527
1.0886









P11
LDGV
81-82
0.35194
0.23815
-8.5826
0.7688
4.8431
-0.79
0.1979
2.4979
0.684
4.4696
3.7564
0.8973
1.4384








P12
LDGV
81-82
0.2927
0.17049
-9.8733
0.9681
2.776
-2.0862
0.4024
1.4025
2.1207
3.7386
2.3379
1.5715
0.6911
2.5825







P13
LDGV
81-82
0.18109
0.07569
-4.5202
0.7632
1.5888
1.3953
0.0498
2.8038
-0.6444
2.0452
0.4039
1.7917
0.6671
1.3367
2.4626






P14
LDGV
81-82
0.14099
0.02431
-4.1674
0.7506
1.6956
1.9641
0.1583
1.4761
-0.6375
1.7248
-0.4851
2.7419
0.5236
0.9627
1.8418
1.5633





P15
LDGV
81-82
0.08737
0.01811
-1.382
0.8401
1.3947
1.6138
-0.2041
1.0019
0.7778
0.8836
1.2699
0.6037
0.6393
0.8763
0.6065
0.8832
1.7403




P16
LDGV
81-82
0.07312
0.0041
-1.1445
0.8151
1.058
1.5224
0.0582
0.8216
1.2094
0.5747
1.1231
0.8514
0.7173
0.8313
0.6082
0.7402
1.2902
1.5334



P17
LDGV
81-82
0.07264
0.00367
-0.9811
0.8032
1.0413
1.4118
0.1444
0.848
1.1681
0.6401
1.1443
0.7002
0.7202
0.816
0.6414
0.7463
1.2653
1.4284
0.5328


P18
LDGV
81-82
0.04243
0.00563
0.017
0.7339
0.8526
1.377
0.2716
1.0442
0.6919
0.8178
0.9258
0.5464
0.7223
0.7683
0.6426
0.8313
0.8107
1.4947
0.4033
0.8387

P19
LDGV
81-82
0.01342
0.00395
0.7869
0.7185
0.788
0.8447
0.6524
0.7441
0.6472
0.857
0.6045
0.7562
0.6997
0.7449
0.6929
0.7279
0.7185
0.7737
0.6197
0.7165
0.6907
IM147 Phase 2 Repression Coefficients, NOx, 1981 to 1982 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
81-82
0.59924
0.60079










2.8438








B12
LDGV
81-82
0.50466
0.48014










1.6
3.6643







B13
LDGV
81-82
0.2646
0.11652










0.8595
2.316
3.4315






B14
LDGV
81-82
0.21288
0.04456










0.7104
1.676
2.6006
2.1072





B15
LDGV
81-82
0.13034
0.02244










0.8971
1.4034
0.8839
1.2572
2.3849




B16
LDGV
81-82
0.1083
0.01418










1.0311
1.1362
1.0575
0.9885
1.7362
2.0954



B17
LDGV
81-82
0.10478
0.0082










1.0248
1.1015
1.1113
1.0374
1.6264
1.8908
1.4012


B18
LDGV
81-82
0.06108
-0.00004










0.9845
1.139
0.8671
1.145
0.9874
2.2913
0.654
1.1584

B19
LDGV
81-82
0.01536
0.00328










0.9626
1.0202
0.9556
0.9706
0.9459
1.0635
0.9025
0.9818
0.9686

-------
IM147 Composite Regression Coefficients, NOx, 1983 to 1985 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
83-85
0.96463
1.88521
18.7418


















P2
LDGV
83-85
0.58177
0.74711
0.782
4.5182















. -

P3
LDGV
83-85
0.57164
0.69924
1.7395
3.8215
3.9404
















P4
LDGV
83-85
0.56866
0.69182
2.2485
3.7307
2.712
4.1135















P5
LDGV
83-85
0.54912
0.68329
0.0309
3.6538
-0.5552
2.7207
4.4539














P6
LDGV
83-85
0.54056
0.65766
-0.2605
3.6825
-0.6893
0.2755
3.3123
6.9261













P7
LDGV
83-85
0.52478
0.64771
-1.2424
3.0981
-0.7131
-0.3096
1.2743
8.0479
3.3901












P8
LDGV
83-65
0.49039
0.64355
-0.2025
2.5593
-0.8873
-2.2555
0.9327
0.9917
2.648
6.9554











P9
LDGV
83-85
0.49081
0.6366
-0.2479
2.5692
-0.9586
-2.1078
0.7709
0.8195
2.6479
6.804
0.8258

\








P10
LDGV
83-85
0.48198
0.62502
-0.6852
2.5126
-1.7481
-0.7564
0.3327
1.3097
1.8572
6.9911
-1.6919
3.7996
/








P11
LDGV
83-85
0.43238
0.38649
0.6471
1.837
-1.1074
0.237
1.3594
1.4974
0.1122
6.1798
0.33
1.9052
I 1.0346








P12
LDGV
83-85
0.35372
0.24907
1.0365
1.599
-1.5529
1.0484
1.5953
-0.2612
1.2403
3.835
-0.566
1.7683
i 0.4528
3.3405







P13
LDGV
83-85
0.22239
0.11604
1.4435
0.8789
1.1136
3.1005
0.6475
1.7479
0.451
1.0221
0.5773
0.455
.0.4514
1.7854
2.2469






P14
LDGV
83-85
0.16685
0.04792
1.1199
0.8083
0.993
2.3512
0.3301
2.1868
0.9638
0.6226
1.2885
1.0545
L 0.5328
0.7862
1.2774
2.0142





P15
LDGV
83-85
0.09344
0.00442
0.6972
0.724
1.0561
1.1547
0.647
1.6301
0.8766
0.4133
1.7045
0.185
: 0.7132
0.6701
0.6719
1.0248
1.5987




P16
LDGV
83-85
0.07618
0.01155
1.4799
0.6974
0.9307
0.8525
0.7603
0.9115
1.0175
0.3596
1.3353
0.4874
10.7501
0.6536
0.7419
0.8969
1.1713
1.4857



P17
LDGV
83-85
0.07136
0.00873
1.0722
0.7157
0.74
1.0551
0.6004
0.9688
0.8579
0.6022
0.9517
0.6109
10.7611
0.5992
0.7747
0.9329
1.1217
1.2072
1.6127


P18
LDGV
83-85
0.03952
0.0055
0.6259
0.6692
0.8058
1.0136
0.6208
1.1765
0.512
0.9231
0.7662
0.5965
10.7047
0.7879
0.6504
0.8038
0.8335
1.1663
1.2587
0.9322

P19
LDGV "
83-85
0.01224
-0.00128
0.8422
0.7007
0.7898
0.724
0.7372
0.8013
0.7199
0.7142
0.6943
0.7149
; 0.7141
0.7285
0.7042
0.7381
0.7145
0.6689
0.7795
0.691
0.7508
IM147 Phase 2 Regression Coefficients, NOx, 1983 to 1985 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5 "
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19 ,
B11
LDGV
83-85
0.73374
0.81776










.2.4382








B12
LDGV
83-85
0.54834
0.39309










' 1.1577
5.767




-


B13
LDGV
83-65
0.31629
0.20822










0.6375
2.8068
3.1803






B14
LDGV
83-85
0.24412
0.1176










(0.8028
1.3747
1.9218
2.6998





B15
LDGV
83-85
0.13375
0.03665










it 0.9668
1.0509
0.9033
1.3245
2.2612




B16
LDGV
83-85
0.10708
0.03554










111.0501
0.8984
1.0487
1.1265
1.6402
2.0329



B17
LDGV
83-85
0.09639
0.02013










it 1.0496
0.8104
1.0655
1.2345
1.5252
1.6719
2.3989


B18
LDGV
83-85
0.05614
0.0163










it 0.9253
1.1246
0.8614
1.096
1.1113
1.8248
1.4559
1.1912

B19
LDGV
83-85
0.01369
0.0022










: 0.9686
0.9844
0.9642
0.9894
0.962
0.9116
1.1138
0.9386
1.0241

-------
IM147 Composite Regression Coefficients, NOx, 1986 to 1989 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
86-89
0.78565
1.40846
22.9983


















P2
LDGV
86-89
0.57844
0.77237
9.21
3.0925

















P3
LDGV
86-89
0.53576
0.63136
9.1703
2.0307
7.0173
















P4
LDGV
86-89
0.52985
0.61276
9.5621
2.0154
5.1612
5.5354















P5
LDGV
86-89
0.48733
0.58411
7.6422
1.5437
3.1981
4.1123
5.8762














P6
LDGV
86-89
0.469
0.58
4.0676
1.525
2.9935
0.4973
4.8986
10.1283













P7
LDGV
86-89
0.44452
0.51948
2.5722
1.2222
2.2925
1.1125
2.3363
11.7286
3.4574












P8
LDGV
86-89
0.42664
0.46953
3.5526
1.2073
1.7483
-0.3064
2.0313
6.7259
2.6637
5.3318











P9
LDGV
86-89
0.42322
0.45426
3.3018
1.301
1.139
0.1369
1.5033
5.9904
2.5273
4.8613
3.2834










P10
LDGV
86-89
0.42048
0.44434
2.7682
1.279
0.8669
0.8265
1.1624
5.9839
1.9554
4.9824
2.1016
2.3949









P11
LDGV
86-89
0.3185
0.16273
1.6969
0.9234
0.2926
1.5843
1.5047
5.7857
0.6453
3.2517
2.4719
1.4771
1.3713








P12
LDGV
86-89
0.28211
0.13132
0.4472
0.9805
-0.3268
1.5058
1.3641
3.9271
1.2167
1.5887
1.8636
1.9637
0.9702
2.2297







P13
LDGV
86-89
0.17181
0.05351
1.5839
0.7587
0.7909
1.2166
0.7145
1.5691
0.9527
1.0643
1.0876
0.6012
0.7526
1.15
2.3208






P14
LDGV
86-89
0.13065
0.04173
0.3316
0.7154
1.0709
0.6518
0.7486
1.2646
0.8718
0.8749
0.8522
0.9529
0.6065
0.9816
1.5163
1.6439





P15
LDGV
86-69
0.08069
0.01822
0.1209
0.6879
0.733
1.0408
0.7212
1.5437
0.8545
0.957
0.2801
0.7471
0.6975
0.8112
0.8905
1.0278
1.3107




P16
LDGV
86-89
0.06908
0.01317
0.5535
0.7013
0.7623
0.918
0.6392
1.068
0.9735
0.7864
0.2568
0.9626
0.7416
0.7472
0.8532
0.8939
1.013
1.487



P17
LDGV
86-89
0.06367
0.01125
0.3901
0.7232
0.6166
0.8153
0.6598
1.0147
0.8687
0.8487
0.2077
0.8136
0.7444
0.7667
0.847
0.9405
0.9462
1.3241
1.4006


P18
LDGV
86-89
0.03823
0.00394
0.7351
0.6866
0.7439
1.0847
0.6838
1.2044
0.6292
0.8002
0.5291
0.672
0.6852
0.8818
0.7191
0.82
0.7757
1.2183
0.9059
0.8386

P19
LDGV
86-89
0.01005
0.00003
0.7593
0.7055
0.7238
0.7901
0.6814
0.9355
0.7038
0.7211
0.6966
0.7005
0.7134
0.7342
0.7231
0.7021
0.7072
0.7254
0.7597
0.7156
0.7326
IM147 Phase 2 Regression Coefficients, NOx, 1986 to 1989 Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
86-89
0.52925
0.42098










2.5542








B12
LDGV
86-89
0.43224
0.27635










1.6545
3.9022







B13
LDGV
86-89
0.23852
0.0957










1.0646
1.7542
3.3487






B14
LDGV
86-89
0.18118
0.07257










0.867
1.4108
2.1959
2.261





B15
LDGV
86-89
0.11203
0.02849










0.9505
1.1788
1.2539
1.4295
1.7812




B16
LDGV
86-89
0.0963
0.02351










1.0428
0.9898
1.2047
1.2246
1.4141
1.9452



B17
LDGV
86-89
0.08734
0.01558










1.0275
1.0114
1.167
1.2958
1.2756
1.7751
1.9705


B18
LDGV
86-89
0.05354
0.0073










0.9133
1.2514
0.967
1.1377
1.0194
1.8001
1.2499
1.0893

B19
LDGV
86-89
0.01153
0.00104










0.964
0.9967
0.973
0.9592
0.9531
0.9973
1.0544
0.9572
1.0152

-------

M147 Composite Regression Coefficients, NOx, 1990 to 1995 Model Year LDGV's





Repress
on Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
90-95
0.85265
0.98234
27.2602


















P2
LDGV
90-95
0.55676
0.37911
3.4685
3.9198

















P3
LDGV
90-95
0.5187
0.31019
2.4287
2.702
6.9157







!








P4
LDGV
90-95
0.51364
0.29256
2.566
2.669
5.4346
4.5289






:








P5
LDGV
90-95
0.47026
0.27475
0.7261
2.0325
3.4764
2.5823
7.1639














P6
LDGV
90-95
0.46835
0.27776
0.0795
2.0153
3.3673
1.5718
6.6904
3.6255




!








P7
LDGV
90-95
0.43598
0.24653
-0.2787
1.6476
1.7967
2.607
2.986
4.5264
4.7362



:








P8
LDGV
90-95
0.42397
0.22613
0.8668
1.576
1.4199
1.0315
2.7698
1.4176
3.8634
4.4221











P9
LDGV
90-95
0.41414
0.21779
0.4251
1.6155
0.9945
0.7619
2.1999
0.108
3.4947
3.5743
5.7316










P10
LDGV
90-95
0.40712
0.2139
-0.4251
1.6685
0.371
1.1081
1.7587
0.4377
2.6567
3.7398
4.0116
3.1506
.<








P11
LDGV
90-95
0.32541
0.07608
0.4951
1.01
0.8419
0.3247
2.3515
0.3887
1.0667
2.0267
3.9523
1.1292
I 1.4038








P12
LDGV
90-95
0.27382
0.05866
1.1507
0.9457
0.7041
-0.1603
1.9823
-0.7329
1.4374
0.757
2.5555
1.2857
< 0.8276
3.2666







P13
LDGV
90-95
0.1552
0.0465
1.245
0.7538
1.2793
0.8753
0.6342
0.2174
0.6015
0.6589
2.2733
-0.1902
I 0.7277
1.3434
2.4018






P14
LDGV
90-95
0.11701
0.02975
1.1371
0.7436
0.97
0.8665
0.7991
0.5198
0.7343
0.5517
1.7367
0.4654
0.6168
1.0241
1.4687
1.7969





P15
LDGV
90-95
0.07219
0.00927
0.8725
0.7153
0.8392
0.9635
0.7945
0.6765
0.7708
0.6798
1.0226
0.5631
r 0.7013
. 0.8864
0.8237
1.0439
1.3295




P16
LDGV
90-95
0.06398
0.00808
1.0798
0.7045
0.8792
0.8433
0.7229
0.5834
0.8931
0.5466
0.9541
0.7035
I 0.7254
0.8265
0.8134
0.9715
1.0589
1,191



P17
LDGV
90-95
0.05809
0.00266
0.7561
0.7292
0.6974
0.8682
0.8458
0.6179
0.8355
0.681
0.8312
0.5502
[ 0.7239
0.822
0.8206
0.9771
0.9638
1.1024
1.5163


P18 '
LDGV
90-95
0.03251
0.00233
0.6937
0.7144
0.6856
0.9367
0.7529
0.9301
0.619
0.8318
0.7798
0.5858
I 0.6898
0.8309
0.7014
0.849
0.7786
1.1002
0.83
0.8865

P19
LDGV
90-95
0.00903
0.00112
0.8195
0.7065
0.733
0.7558
0.7072
0.8124
0.7212
0.7331
0.7165
0.7193
f 0.7121
0.7215
0.7187
0.7075
0.7149
0.7316
0.7374
0.7123
0.7172
IM147 Phase 2 Regression Coefficients, NOx, 1990 to 1995 Model Year LDGV's





Rearession Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
90-95
0.4955
0.16783










i 2.7648








B12
LDGV-
90-95
0.39806
0.10265










! 1.5279
4.9874


-




B13
LDGV
90-95
0.21598
0.0764










. 0.9775
2.036
3.2274






B14
LDGV
90-95
0.16191
0.04867










I 0.8655
1.4947
2.0303
2.4727





B15
LDGV
90-95
0.09916
0.01709










f 0.9685
1.262
1.1221
1.4177
1.8321




B16
LDGV
90-95
0.08828
0.01425










I 1.0137
1.1226
1.1413
1.3001
1.4762
1.565



B17
LDGV
90-95
0.07957
0.00619










[ 0.9986
1.1256
1.132
1.322
1.3198
1.5162
2.0499


B18
LDGV
90-95
0.04511
0.00552










0.9273
1.1868
 0.9219
1.1679
1.0538
1.5928
1.0608
1.1805

B19
LDGV
90-95
0.01076
0.00217










f 0.9663
0.9854
0.9735
0.9568
0.9719
1.0002
1.022
0.9682
0.9807

-------
IM
147 Composite Regression Coefficients, NOx, 1996 and Newer Model Year LDGV's





Regression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
P1
LDGV
96+
0.29865
0.37011
11.7736


















P2
LDGV
96+
0.22677
0.24297
-3.0327
2.4026

















P3
LDGV
96+
0.21591
0.222
-0.4545
1.4421
5.448
















P4
LDGV
96+
0.21456
0.2173
-0.7302
1.4606
4.3233
3.1071















P5
LDGV
96+
0.20702
0.21364
1.0698
1.2242
3.8494
-0.6109
4.3718














P6
LDGV
96+
0.20746
0.2141
0.6213
1.2154
3.841
-0.6842
4.2145
0.9383













P7
LDGV
96+
0.20382
0.20285
2.5242
1.0207
3.6971
-1.4265
3.1047
0.3945
2.683












P8
LDGV
96+
0.19807
0.19482
4.1563
0.9074
3.4911
-2.0343
2.3803
-0.0536
2.1013
3.3361











P9
LDGV
96+
0.19619
0.19096
1.8739
1.0364
2.2721
-1.119
2.141
-0.5391
2.1297
2.5845
3.318










P10
LDGV
96+
0.1935
0.18673
1.493
0.998
2.3525
-1.4944
2.0734
0.242
1.6938
2.8999
1.2192
1.9845









P11
LDGV
96+
0.15206
0.11563
6.6686
0.3811
2.506
-1.1883
0.7057
-0.2193
2.4209
0.8267
1.5013
0.5498
1.1615








P12
LDGV
96+
0.14242
0.09025
7.3264
0.4696
2.0937
-0.2105
0.3797
-0.6436
2.1032
0.8737
0.8521
0.6422
0.984
1.8962







P13
LDGV
96+
0.1117
0.0621
4.8271
0.6696
2.0895
-2.2591
0.9164
-0.565
1.4492
-0.1321
0.1274
0.7773
0.8735
0.8257
2.0634






P14
LDGV
96+
0.0781
0.04141
3.4134
0.5916
1.3165
-0.2995
1.3675
-0.2089
1.4165
0.0989
0.2565
0.3993
0.7549
0.6841
1.288
1.631





P15
LDGV
96+
0.05148
0.02187
2.4596
0.6558
1.17
0.6931
0.7565
0.2435
0.7215
0.5341
0.5256
0.6843
0.7209
0.6775
1.1075
0.9069
1.122




P16
LDGV
96+
0.04778
0.01987
3.1395
0.6091
1.203
0.5425
0.9077
0.4201
0.7848
0.5764
0.1403
0.7957
0.7431
0.6929
1.0516
0.9276
0.7894
1.4029



P17
LDGV
96+
0.04525
0.01326
3.3943
0.6187
0.8612
0.7678
1.1673
0.4078
0.7682
0.5486
0.2441
0.6409
0.7404
0.726
0.935
1.0264
0.7278
1.3847
1.368


P18
LDGV
96+
0.02353
0.00022
1.5178
0.6979
0.7651
0.9288
0.6991
0.3426
0.7998
0.6796
0.6094
0.5847
0.7147
0.8429
0.6623
0.895
0.6746
0.9795
0.7813
1.0686

P19
LDGV
96+
0.004
0.00038
0.7846
0.7189
0.7176
0.7276
0.6851
0.7511
0.7279
0.6879
0.7681
0.6817
0.7174
0.7317
0.7291
0.7203
0.7125
0.7336
0.7584
0.7113
0.7181
I
M147 Phase 2 Regression Coefficients, NOx, 1996 and Newer Model Year LDGV's





Repression Coefficients
Segment
Number


RMS
Error
Reg.
Constant
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
B11
LDGV
96+
0.20953
0.18322










1.6724








B12
LDGV
96+
0.19448
0.14434










1.3687
2.7382







B13
LDGV
96+
0.155
0.09509










1.1262
1.2948
2.6037






B14
LDGV
96+
0.10659
0.06065










0.9685
0.9898
1.6977
2.2378





B15
LDGV
96+
0.06912
0.03298










0.9526
0.8932
1.4872
1.252
1.5305




B16
LDGV
96+
0.0649
0.03049










0.9772
0.9069
1.4267
1.2614
1.1325
1.7334



B17
LDGV
96+
0.06203
0.02204










0.9694
0.9325
1.3118
1.3384
1.0793
1.6717
1.6273


B18
LDGV
96+
0.03172
0.00214










0.9508
1.1071
0.9089
1.1952
0.9329
1.2914
1.0022
1.4675

B19
LDGV
96+
0.00523
0.00111










0.9672
0.9874
0.9855
0.9791
0.9675
0.9838
1.0369
0.9689
0.9757

-------
Appendix D
Excess Emissions with Fast-pass, Retest, Fast-Fail Enabled
Max CO Outpoints


Excess
Excess HC

Excess
Excess CO

Excess
Excess NO

Vehicle
Model Year
IM240 HC
with fast-
Excess HC
IM240 CO
with last-
Excess CO
IM240 NOx
with last-
Excess NOx
Class
Range
(grams)
pass (grams)
Identified
(grams)
pass (grams)
Identified
(grams)
pass (grams)
Identified
LDGV
81-82
0
0
N/A
56.74
49.73
87.6%
3.61
3.61
100.0%
83-85
6.13
6.13
100.0%
243.24
237.02
97.4%
3.49
3.49
100.0%
86-89
10.72
10.65
99.3%
301.8
294.27
97.5%
9.92
9.92
100.0%
90-95
8.96
8.68
96.9%
185.26
157.96
85.3%
6.59
6.02
91.4%
96+
0
0
N/A
0
0
N/A
0
0
N/A
ALL
25.81
25.46
98.6%
787.04
738.98
93.9%
23.61
23.04
97.6%
LDGT1
81-85
10.99
. 8.32
75.7%
605.55
590.71
97.5%
19.02
18.72
98.4%
88-89
5.55
5.13
92.4%
533.37
485.61
91.0%
2.37
2.36
99.6%
90-95
0.42
0
0.0%
1.77
0
0.0%
1.65
1.65
100.0%
96+
0
0
N/A
0
0
N/A
0.43
0.43
100.0%
ALL
16.96
13.45
79.3%
1140.69
1076.32
94.4%
23.47
23.16
98.7%
LDGT2
81-85
15.62
15.62
100.0%
441.44
441.44
100.0%
6.69
6.69
100.0%
86-87
10.39
10.39
100.0%
285.54
285.54
100.0%
4.11
2.91
70.8%
88-95
0.21
0.21
100.0%
26.15
15.16
58.0%
0.68
0.27
39.7%
96+
0
0
N/A
0
0
N/A
0
0
N/A
ALL
26.22
26.22
100.0%
753.13
742.14
98.5%
11.48
9.87
86.0%
Total
ALL
68.99
65.13
94.4%
2680.86
2557.44
95.4%
58.56
56.07
95.7%
D-l

-------
Excess Emissions with Fast-pass Enabled
Max CO Cutpoints


Excess
Excess HC

Excess
Excess CO

Excess
Excess NO

Vehicle
Model Year
IM240 HC
with fast-
Excess HC
IM240 CO
with fast-
Excess CO
IM240 Nox
with fast-
Excess NOx
Class
Range
(grams)
pass (grams)
Identified
(grams)
pass (grams)
Identified
(grams)
pass (grams)
Identified
LDGV
81-82
0
0
N/A
56.74
49.73
87.6%
3.61
3.61
100.0%
83-85
6.13
6.13
100.0%
243.24
237.02
97.4%
3.49
3.49
100.0%
86-89
10.72
10.65
99.3%
301.8
294.27
97.5%
9.92
9.92
100.0%
90-95
8.96
8.68
96.9%
185.26
157.96
85.3%
6.59
6.02
91.4%
96+
0
0
N/A
0
0
N/A
0
0
N/A
ALL
25.81
25.46
98.6%
787.04
738.98
93.9%
23.61
23.04
97.6%
LDGT1
81-85
10.99
8.32
75.7%
605.55
560.92
92.6%
19.02
18.72
98.4%
88-89
5.55
5.13
92.4%
533.37
485.61
91.0%
2.37
1.69
71.3%
90-95
0.42
0
0.0%
1.77
0
0.0%
1.65
1.65
100.0%
96+
0
0
N/A
0
0
N/A
0.43
0
0.0%
ALL
16.96
13.45
79.3%
1140.69
1046.53
91.7%
23.47
22.06
94.0%
LDGT2
81-85
15.62
14.47
92.6%
441.44
441.44
100.0%
6.69
5.72
85.5%
86-87
10.39
10.39
100.0%
285.54
285.54
100.0%
4.11
2.31
56.2%
88-95
0.21
0.21
100.0%
26.15
15.16
58.0%
0.68
0.27
39.7%
96+
0
0
N/A
0
0
N/A
0
0
N/A
ALL
26.22
25.07
95.6%
753.13
742.14
98.5%
11.48
8.3
72.3%
Total
ALL
68.99
63.98
92.7%
2680.86
2527.65
94.3%
58.56
53.4
91.2%
D-2

-------
Excess Emissions with Fast-pass Disabled
Max CO Cutpoints


Excess
Excess HC

Excess
Excess CO

Excess
Excess NO

Vehicle
Model Year
IM240 HC
w/o fast-
Excess HC
IM240 CO
w/o fast-
Excess CO
IM240 Nox
w/o fast-
Excess NOx
Class
Range
(grams)
pass (grams)
Identified
(grams)
pass (grams)
Identified
(grams)
pass (grams)
Identified
LDGV
81-82
10.99
8.32
75.7%
605.55
603.26
99.6%
19.02
18.72
98.4%
83-85
5.55
5.55
100.0%
533.37
527.14
98.8%
2.37
1.69
71.3%
86-89
0.42
0.42
100.0%
1.77
0
0.0%
1.65
1.65
100.0%
90-95
0
o 
N/A
0
0
N/A
0.43
0.43
100.0%
96+
16.96
14.29
84.3%
1140.69
1130.4
99.1%
23.47
22.49
95.8%
ALL
15.62
14.47
92.6%
441.44
441.44
100.0%
6.69
5.72
85.5%
LDGT1
81-85
10.39 
10.39
100.0%
285.54
285.54
100.0%
4.11
2.31
56.2%
88-89
0.21
0.21
100.0%
26.15
26.15
100.0%
0.68
0.38
55.9%
90-95
0
0
N/A
0
0
N/A
0
0
N/A
96+
26.22
25.07
95.6%
753.13
753.13
100.0%
11.48
8.41
.73.3%
ALL
0
0
N/A
56.74
49.73
87.6%
3.61
3.61
100.0%
LDGT2
81-85
6.13
6.13
100.0%
243.24
237.02
97.4%
3.49
3.49
100.0%
86-87
10.72
10.72
100.0%
301.8
301.22
99.8%
9.92
9.92
100.0%
88-95
8.96
8.87
99.0%
185.26
160
86.4%
6.59
6.02
91.4%
96+
0
0
N/A
0
0
N/A
0
0
N/A
ALL
25.81
25.72
99.7%
787.04
747.97
95.0%
23.61
23.04
97.6%
Total
ALL
68.99
65.08
94.3%
2680.86
2631.5
98.2%
58.56
53.94
92.1%
D-3

-------
IM147 CUMULATIVE POSITIVE POWER (CPP) VARIATION OUTPOINTS
IM147 REFERENCE DATA	POWER VARIATION OUTPOINTS (mph2/sec)

SPEED
CPP
"BASE"
MULT.
VARYING
CPP LIMITS

(mph)
fmph2/sec)
DELTA
FACTOR
DELIA
LOW
HIGH
0
0.0
0.00





1
0.0
0.00





2
0.0
0.00





3
0.0
0.00





4
0.0
0.00





5
3.3
10.89





6
6.6
43.56





7
9.9
98.01





8
13.2
174.24





9
16.5
272.25





10
19.8
. 392.04





11
22.2
492.84





12
24.3
590.49





13
25.8
665.64





14
26.4
696.96





15
25.7
696.96





16
25.1
696.96





17
24.7
696.96





18
25.2
721.91





19
25.4
732.03





20
27.2
826.71





21
26.5
826.71





22
24.0
826.71





23
22.7
826.71





24
19.4
826.71





25
17.7
826.71





26
17.2
826.71





27
18.1
858.48





28
18.6
876.83.





29
20.0
930.87





30
20.7
959.36
69.7
3.500
244.0
715.31
1,203.41
31
21.7
1001.76
72.8
3.424
249.3
752.44
1,251.08
32
22.4
1032.63
75.1
3.386
254.2
778.47
1,286.79
33
22.5
1037.12
75.4
3.348
252.4
784.71
1,289.53
34
22.1
1037.12
75.4
3.348
252.4
784.71
1,289.53
35
21.5
1037.12
75.4
3.348
252.4
784.71
1,289.53
36
20.9
1037.12
75.4
3.348
252.4
784.71
1,289.53
37
20.4
1037.12
75.4
3.348
252.4
784.71
1,289.53
38
19.8
1037.12
75.4
3.348
252.4
784.71
1,289.53
39
17.0
1037.12
75.4
3.348
252.4
784.71
1,289.53
40
17.1
1040.53
75.6
3.311
250.4
790.16
1,290.90
41
15.8
1040.53
75.6
3.311
250.4
790.16
1,290.90
42
15.8
1040.53
75.6
3.311
250.4
790.16
1,290.90
43
17.7
1104.18
80.3
3.273
262.7
841.53
1,366.83
44
19.8
1182.93
86.0
3.235
278.1
904.80
1,461.06
45
21.6
1257.45
91.4
3.197
292.2
965.27
1,549.63
E-1

-------
IM147 CUMULATIVE POSITIVE POWER (CPP) VARIATION OUTPOINTS
IM147 REFERENCE DATA
TIME SPEED CPP

(mph)
(mph2/sec)
46
22.2
1283.73
47
24.5
1391.14
48
24.7
1400.98
49
24.8
1405.93
50
24.7
1405.93
51
24.6
1405.93
52
24.6
.1405.93
53
25.1
1430.78
54
25.6
1456.13
55
25.7
1461.26
56
25.4
1461.26
57
24.9
1461.26
58
25.0
1466.25
59
25.4
1486.41
60
26.0
1517.25
61
26.0
1517.25
62
25.7
1517.25
63
26.1
1537.97
64
26.7
1569.65
65
27.3
1602.05
66
30.5
1787.01
67
33.5
1979.01
68
36.2
2167.20
69
37.3
2248.05
70
39.3
2401.25
71
40.5
2497.01
72
42.1
2629.17
73
43.5
2749.01
74
45.1
2890.77
75
46.0
2972.76
76
46.8
3047^00
77
47.5
3113.01
78
47.5
3113.01
79
47.3
3113.01
80
47.2
3113.01
81
47.2
3113.01
82
47.4
3131.93
83
47.9
3179.58
84
48.5
3237.42
85
49.1
3295.98
86
49.5
3335.42
87
50.0
3385.17
88
50.6
3445.53
89
51.0
3486.17
90
51.5
3537.42
91
52.2
3610.01
POWER VARIATION OUTPOINTS (mph2/sec)
"BASE" MULT. VARYING CPP LIMITS
LTA
FACTOR
DELTA
LOW
HIGH
93.3
3.159
294.8
988.97
1,578.49
101.1
3.121
315.6
1,075.55
1,706.73
101.8
3.083
314.0
1,087.02
1,714.94
102.2
3.045
311.2
1,094.73
1,717.13
102.2
3.045
311.2
1,094.73
1,717.13
102.2
3.045
311.2
1,094.73
1,717.13
102.2
3.045
311.2
1,094.73
1,717.13
104.0
3.008
312.8
1,118.02
1,743.54
105.8
2.970
314.3
1,141.83
1,770.43
106.2
2.932
311.4
1,149.88
1,772.64
106.2
2.932
311.4
1,149.88
1,772.64
106.2
2.932
311.4
1,149.88
1,772.64
106.6
2.894
308.4
1,157.84
1,774.66
108.0
2.856
308.6
1,177.85
1,794.97
110.3
2.818
310.8
1,206.47
1,828.03
110.3
2.818
310.8
1,206.47
1,828.03
110.3
2.818
310.8
1,206.47
1,828.03
111.8
2.780
310.8
1,227.18
1,848.76
114.1
2.742
312.9
1,256.78
1,882.52
116.4
2.705
314.9
1,287.13
1,916.97
129.9
2.667
346.4
1,440.65
2,133.37
143.8
2.629
378.1
1,600.89
2,357.13
157.5
2.591
408.1
1,759.09
2,575.31
163.4
2.553
417.1
1,830.90
2,665.20
174.5
2.515
439.0
1,962.29
2,840.21
181.5
2.477
449.6
2,047.41
2,946.61
191.1
2.439
466.2
2,163.02
3,095.32
199.8
2.402
479.8
2,269.18
3,228.84
210.1
2.364
496.6
2,394.15
3,387.39
216.1
2.326
502.5
2,470.24
3,475.28
221.5
2.288
506.7
2,540.32
3,553.68
226.3
2.250
509.1
2,603.92
3,622.10
226.3
2.250
509.1
2,603.92
3,622.10
226.3
2.250
509.1
2,603.92
3,622.10
226.3
2.250
509.1
2,603.92
3,622.10
226.3
2.250
509.1
2,603.92
3,622.10
227.6
2.212
503.6
2,628.37
3,635.49
231.1
2.174
502.5
2,677.11
3,682.05
235.3
2.136
502.7
2,734.73
3,740.11
239.6
2.098
502.7
2,793.27
3,798.69
242.4
2.061
499.5
2,835.88
3,834.96
246.0
2.023
497.7
2,887.49
3,882.85
250.4
1.985
497.1
2,948.47
3,942.59
253.4
1.947
493.3
2,992.84
3,979.50
257.1
1.909
490.8
3,046.58
4,028.26
262.4
1.871
491.0
3,119.03
4,100.99

-------
IM147 CUMULATIVE POSITIVE POWER (CPP) VARIATION OUTPOINTS
IM147 REFERENCE DATA
TIME SPEED CPP
J
(mpM
(mph2/sec)
92
53.2
3715.41
93
54.1
3811.98
94
54.6
3866.33
95
54.9
3899.18
96
55.0
3910.17
97
54.9
3910.17
98
54.6
3910.17
99
54.6
3910.17
100
54.8
3932.05
101
55.1
3965.02
102
55.5
4009.26
103
55.7
4031.50
104
56.1
4076.22
105
56.3
4098.70
106
56.6
4132.57
107
56.7
4143.90
108
56.7
4143.90
109
56.3
4143.90
110
56.0
4143.90
111
55.0
4143.90
112
53.4
4143.90
113
51.6
4143.90
114
51.8
4164.58
115
52.1
4195.75
116
52.5
4237.59
117
53.0
4290.34
118
53.5
4343.59
119
54.0
4397.34
120
54.9
4495.35
121
55.4
4550.50
122
55.6
4572.70
123
56.0
4617.34
124
56.0
4617.34
125
55.8
4617.34
126
55.2
4617.34
127
54.5
4617.34
128
53.6
4617.34
129
52.5
4617.34
130
51.5
4617.34
131
50.5
4617.34
132
48.0
4617.34
133
44.5
4617.34
134
41.0
4617.34
135
37.5
4617.34
136
34.0
4617.34
137
30.5
4617.34
POWER VARIATION OUTPOINTS (mph2/sec)
"BASE" MULT. VARYING CPP LIMITS
DELTA
FACTOR
DELTA
LOW
HIGH
270.0
1.833
495.1
3,220.33
4,210.49
277.1
1.795
497.5
3,314.53
4,309.43
281.0
1.758
493.9
3,372.43
4,360.23
283.4
1.720
487.4
3,411.82
4,386.54
284.2
1.682
478.0
3,432.20
4,388.14
284.2
1.682
478.0
3,432.20
4,388.14
284.2
1.682
478.0
3,432.20
4,388.14
284.2
1.682
478.0
3,432.20
4,388.14
285.8
1.644
469.8
3,462.23
4,401.87
288.2
1.606
462.8
3,502.17
4,427.87
291.4
1.568
457.0
3,552.29
4,466.23
293.0
1.530
448.4
3,583.09
4,479.91
296.3
1.492
442.2
3,634.06
4,518.38
297.9
1.455
433.3
3,665.39
4,532.01
300.4
1.417
425.5
3,707.05
4,558.09
301.2
1.379
415.3
3,728.63
4,559.17
301.2
1.379
415.3
3,728.63
4,559.17
301.2
1.379
415.3
3,728.63
4,559.17
301.2
1.379
415.3
3,728.63
4,559.17
301.2
1.379
415.3
3,728.63
4,559.17
301.2
1.379
415.3
3,728.63
4,559.17
301.2
1.379
415.3
3,728.63
4,559.17
302.7
1.341
405.9
3,758.70
4,570.46
305.0
1.303
397.4
3,798.38
4,593.12
308.0
1.265
389.7
3,847.93
4,627.25
311.8
1.227
382.7
3,907.64
4,673.04
315.7
1.189
375.5
3,968.10
4,719.08
319.6
1.152
368.0
4,029.31
4,765.37
326.7
1.114
363.9
4,131.49
4,859.21
330.7
1.076
355.8
4,194.70
4,906.30
332.4
1.038
344.9
4,227.76
4,917.64
335.6
1.000
335.6
4,281.74
4,952.94
335.6
1.000
335.6
4,281.74
4,952.94
335.6
1.000
335.6
4,281.74
4,952.94
335.6
1.000
335.6
4,281.74
4,952.94
335.6
1.000
335.6
4,281.74
4,952.94
335.6
1.000
335.6
4,281.74
4,952.94
335.6
1.000
335.6
4,281.74
4,952.94
335.6
1.000
335.6
4,281.74
4,952.94
335.6
1.000
335.6
4,281.74
4,952.94
335.6
1.000
335.6
4,281.74
4,952,94
335.6
.1.000
335.6
4,281.74
4,952.94.
335.6
1.000
335.6
4,281.74
4,952.94
335.6
1.000
335.6
4,281.74
4,952.94
335.6
1.000
335.6
4,281.74
4,952.94
335.6
1.000
335.6
4,281.74
4,952.94
E-3

-------
IM147 CUMULATIVE POSITIVE POWER (CPP) VARIATION OUTPOINTS
IM147 REFERENCE DATA	POWER VARIATION OUTPOINTS (mph2/sec)
TIME
SPEED
CPP
"BASE"
MULT.
VARYING
CPP LIMITS
(sec)
(mph)
(mph2/sec)
DELTA
FACTOR
DELTA
LOW
HIGH
138
27.0
4617.34
335.6
1.000
335.6
4,281.74
4,952.94
139
23.5
4617.34
335.6
1.000
335.6
4,281.74
4,952.94
140
20.0
4617.34
335.6
1.000
335.6
4,281.74
4,952.94
141
16.5
4617.34
335.6
1.000
335.6
4,281.74
4,952.94
142
13.0
4617.34
335.6
1.000
335.6
4,281.74
4,952.94
143
9.5
4617.34
335.6
1.000
335.6
4,281.74
4,952.94
144
6.0
4617.34
335.6
1.000
335.6
4,281.74
4,952.94
145
2.5
4617.34
335.6
1.000
335.6
4,281.74
4,952.94
146
0.0
4617.34
335.6
1.000
335.6
4,281.74
4,952.94
Cycle Sums

4617.34

1.000

4,281.74
4,952.94
E-4

-------