ADCO2 Follow-up from April 14, 2011 Meeting

 Follow-up included the following:

 In lieu of acceleration and TRLHP@50 mph as part of the ADCO2 equation study:

 Total cycle energy

 Total cycle power 

 F-terms (not officially requested)

 In lieu of axle ratio as part of the ADCO2equation study N/V

1 Date: May 20, 2011

 In lieu of axle ratio as part of the ADCO2equation study N/V

 Statistically,

 Total cycle and power models had “unacceptable” residuals in 5 of 6 cases

 F-terms (with ALVW test weight) produces best model fits to date

 Axle ratio (vs. N/V) improves model fit (perhaps due to the fact only top gear N/V was 

used for model building)

 Acceleration and TRLHP@50 mph (vs. total cycle power or energy) still a good candidate 

model

 Fuel type indicator variable produces is NOT statistically significant and produces 

unacceptable residuals except when included in the model with the Acceleration and 

TRLHP@50 mph variable

 The following slides support the follow-up
EPA Bag 1 – Energy/Power Calculations

EPA Cycle - Bag 1

20

40

60

80

100

S

p

e

e

d (m

p

h) or P

o

w

er (k

W)

F250 Dyno Data at 8.5K (kW)

Vehicle Speed (mph)

Calculated Pow er (kW) - New Method

Calculated Pow er (kW) - Old Method

2 Date: May 20, 2011

-60

-40

-20

0

0 100 200 300 400 500

Time (sec)

S

p

e

e

d (m

p

h) or P

o

w

er (k

W)

Total Power Positive Time Total Time

ALVW Class F0 F1 F2 Hp MJ Hp MJ Hp MJ kW-hr sec sec

8500 54.67 1.6218 0.03708 Bag 1 17.4 3.8 14.8 3.3 32.2 7.1 296 505

Bag 2 11.7 4.3 5.7 2.1 17.4 6.3 489 867

Bag 3 17.4 3.8 14.8 3.3 32.2 7.1 296 505

Bag 4 (Hwy) 8.7 4.5 27.4 14.1 36.2 18.6 5.18 691 765

City (FTP 75) 49.5 13.4 3.73

M-H 43.5 15.8 4.38

Inertia Roadload Total Energy
EPA Bag 1 – 200 to 300 seconds

EPA Cycle - Bag 1

60

80

100

S

p

e

e

d (m

ph) or P

o

w

er (k

W)

F250 Dyno Data at 8.5K (kW)

Vehicle Speed (mph)

Calculated Pow er (kW) - New Method

Calculated Pow er (kW) - Old Method

3 Date: May 20, 2011

-60

-40

-20

0

20

40

200 210 220 230 240 250 260 270 280 290 300

Time (sec)

S

p

e

e

d (m

ph) or P

o

w

er (k

W)
100

99

98

97

96

95

94

93

92

Non Total C y cle Power or Energy

Total C y cle Power or Energy

Model Evaluation Chart

[16]

[15]

[14]

The ADCO2

equation from 

April 14, 2011 

meeting

• Bracketed numbers are 

hypertext linked to the 

respective model

• Italic brackets [ ] represent 

Proposed

ADCO2 

Model

4 Date: May 20, 2011

1 2 3 4 5

92

91

90

89

88

87

86

85

84

83

82

81

80

Terms in Model

R

^

2

a

dj

[1]

[2]

[3] [4]

[5]

[6]

[7]

[8]

[11]

[9]

[10]

[12]

[13]

• Italic brackets [ ] represent 

models with well-behaved 

residuals
[1] Base CO2 Model

The regression equation is

CO2 = - 895 + 270 Axle Ratio + 3.12 Accel+TRLHP_50 mph

Predictor Coef SE Coef T P VIF

Constant -894.55 92.34 -9.69 0.000

Axle Ratio 270.02 23.24 11.62 0.000 1.047

Accel+TRLHP_50 mph 3.1226 0.2398 13.02 0.000 1.047

S = 47.6565 R-Sq = 89.0% R-Sq(adj) = 88.5%

5 Date: May 20, 2011

-150 -100 -50 0 50 100

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI1

Percent

Mean 3.533209E-13

StDev 46.69

N 51

AD 0.464

P-Value 0.245

Probability Plot of RESI1

Normal

S = 47.6565 R-Sq = 89.0% R-Sq(adj) = 88.5%

Analysis of Variance

Source DF SS MS F P

Regression 2 877854 438927 193.26 0.000

Residual Error 48 109015 2271

Total 50 986868

Source DF Seq SS

Axle Ratio 1 492697

Accel+TRLHP_50 mph 1 385156

Model evaluation chart

Residuals

well-behaved
[2] Base ln(CO2

) Model

The regression equation is

ln CO2 = 4.26 + 0.393 Axle Ratio + 0.00426 Accel+TRLHP_50 mph

Predictor Coef SE Coef T P VIF

Constant 4.2602 0.1145 37.21 0.000

Axle Ratio 0.39324 0.02881 13.65 0.000 1.047

Accel+TRLHP_50 mph 0.0042604 0.0002973 14.33 0.000 1.047

S = 0.0590850 R-Sq = 91.2% R-Sq(adj) = 90.8%

6 Date: May 20, 2011

S = 0.0590850 R-Sq = 91.2% R-Sq(adj) = 90.8%

Analysis of Variance

Source DF SS MS F P

Regression 2 1.73664 0.86832 248.73 0.000

Residual Error 48 0.16757 0.00349

Total 50 1.90421

Source DF Seq SS

Axle Ratio 1 1.01968

Accel+TRLHP_50 mph 1 0.71697

-0.15 -0.10 -0.05 0.00 0.05 0.10 0.15

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI2

Percent

Mean -1.04492E-15

StDev 0.05789

N 51

AD 0.427

P-Value 0.301

Probability Plot of RESI2

Normal

Model evaluation chart

Residuals

well-behaved
[3] CO2 Model – with N/V in lieu of Axle Ratio

The regression equation is

CO2 = - 527 + 21.6 N/V + 3.41 Accel+TRLHP_50 mph

48 cases used, 3 cases contain missing values

Predictor Coef SE Coef T P VIF

Constant -526.66 80.19 -6.57 0.000

N/V 21.587 2.360 9.15 0.000 1.014

Accel+TRLHP_50 mph 3.4084 0.2715 12.56 0.000 1.014

7 Date: May 20, 2011

S = 54.7812 R-Sq = 85.8% R-Sq(adj) = 85.2%

Analysis of Variance

Source DF SS MS F P

Regression 2 815635 407817 135.89 0.000

Residual Error 45 135044 3001

Total 47 950679

Source DF Seq SS

N/V 1 342552

Accel+TRLHP_50 mph 1 473083

-150 -100 -50 0 50 100

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI3

Percent

Mean -1.42109E-14

StDev 53.60

N 48

AD 0.244

P-Value 0.751

Probability Plot of RESI3

Normal

Model evaluation chart

Residuals

well-behaved
[4] ln(CO2

) Model – with N/V in lieu of axle ratio

The regression equation is

ln CO2 = 4.82 + 0.0306 N/V + 0.00469 Accel+TRLHP_50 mph

48 cases used, 3 cases contain missing values

Predictor Coef SE Coef T P VIF

Constant 4.8188 0.1095 43.99 0.000

N/V 0.030574 0.003223 9.49 0.000 1.014

Accel+TRLHP_50 mph 0.0046905 0.0003708 12.65 0.000 1.014

8 Date: May 20, 2011

S = 0.0748206 R-Sq = 86.2% R-Sq(adj) = 85.6%

Analysis of Variance

Source DF SS MS F P

Regression 2 1.57773 0.78886 140.92 0.000

Residual Error 45 0.25192 0.00560

Total 47 1.82964

Source DF Seq SS

N/V 1 0.68179

Accel+TRLHP_50 mph 1 0.89594

-0.2 -0.1 0.0 0.1 0.2

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI4

Percent

Mean 9.251859E-17

StDev 0.07321

N 48

AD 0.252

P-Value 0.724

Probability Plot of RESI4

Normal

Model evaluation chart

Residuals

well-behaved
[5] CO2 Model – with N/V and Total Cycle Power

The regression equation is

CO2 = - 302 + 15.1 N/V + 11.7 Total Cycle Power (Hp)

48 cases used, 3 cases contain missing values

Predictor Coef SE Coef T P VIF

Constant -301.62 64.96 -4.64 0.000

N/V 15.106 2.202 6.86 0.000 1.108

Total Cycle Power (Hp) 11.6857 0.8077 14.47 0.000 1.108

9 Date: May 20, 2011

S = 48.8992 R-Sq = 88.7% R-Sq(adj) = 88.2%

Analysis of Variance

Source DF SS MS F P

Regression 2 843078 421539 176.29 0.000

Residual Error 45 107601 2391

Total 47 950679

Source DF Seq SS

N/V 1 342552

Total Cycle Power (Hp) 1 500526

-100 -50 0 50 100

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI5

Percent

Mean -2.64085E-13

StDev 47.85

N 48

AD 0.730

P-Value 0.053

Probability Plot of RESI5

Normal

Model evaluation chart

Residuals NOT

well-behaved
[6] ln(CO2

) Model – with N/V and Total Cycle Power

The regression equation is

ln CO2 = 5.14 + 0.0220 N/V + 0.0157 Total Cycle Power (Hp)

48 cases used, 3 cases contain missing values

Predictor Coef SE Coef T P VIF

Constant 5.13789 0.09833 52.25 0.000

N/V 0.021995 0.003333 6.60 0.000 1.108

Total Cycle Power (Hp) 0.015681 0.001223 12.83 0.000 1.108

S = 0.0740171 R-Sq(adj) = 85.9%

10 Date: May 20, 2011

S = 0.0740171 R-Sq = 86.5% R-Sq(adj) = 85.9%

Analysis of Variance

Source DF SS MS F P

Regression 2 1.58311 0.79155 144.48 0.000

Residual Error 45 0.24653 0.00548

Total 47 1.82964

-0.2 -0.1 0.0 0.1 0.2

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI6

Percent

Mean -5.92119E-16

StDev 0.07243

N 48

AD 0.812

P-Value 0.033

Probability Plot of RESI6

Normal

Model evaluation chart

Residuals NOT

well-behaved
[7] CO2 Model – with N/V and Total Cycle Energy*

The regression equation is

CO2 = - 196 + 13.3 N/V + 106 Total Cycle Energy (kW-hr)

48 cases used, 3 cases contain missing values

Predictor Coef SE Coef T P VIF

Constant -195.98 73.18 -2.68 0.010

N/V 13.262 2.609 5.08 0.000 1.163

Total Cycle Energy (kW-hr) 105.961 8.792 12.05 0.000 1.163

S = 56.5388 R-Sq(adj) = 84.2%

11 Date: May 20, 2011

S = 56.5388 R-Sq = 84.9% R-Sq(adj) = 84.2%

Analysis of Variance

Source DF SS MS F P

Regression 2 806830 403415 126.20 0.000

Residual Error 45 143849 3197

Total 47 950679

-150 -100 -50 0 50 100

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI7

Percent

Mean -1.85925E-13

StDev 55.32

N 48

AD 0.850

P-Value 0.027

Probability Plot of RESI7

Normal

* Note: Total Cycle Energy in [kW-hr]

Model evaluation chart

Residuals NOT

well-behaved
[8] ln(CO2

) Model – with N/V and Total Cycle Energy*

The regression equation is

ln CO2 = 5.28 + 0.0197 N/V + 0.140 Total Cycle Energy (kW-hr)

48 cases used, 3 cases contain missing values

Predictor Coef SE Coef T P VIF

Constant 5.2825 0.1112 47.49 0.000

N/V 0.019717 0.003966 4.97 0.000 1.163

Total Cycle Energy (kW-hr) 0.14043 0.01336 10.51 0.000 1.163

S = 0.0859402 R-Sq(adj) = 81.0%

12 Date: May 20, 2011

S = 0.0859402 R-Sq = 81.8% R-Sq(adj) = 81.0%

Analysis of Variance

Source DF SS MS F P

Regression 2 1.49729 0.74864 101.36 0.000

Residual Error 45 0.33236 0.00739

Total 47 1.82964

-0.2 -0.1 0.0 0.1 0.2

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI8

Percent

Mean -1.01770E-15

StDev 0.08409

N 48

AD 0.810

P-Value 0.034

Probability Plot of RESI8

Normal

* Note: Total Cycle Energy in [kW-hr]

Model evaluation chart

Residuals NOT

well-behaved
[9] CO2 Model – with Fuel indicator variable

The regression equation is

CO2 = - 411 + 15.6 N/V + 3.55 Accel+TRLHP_50 mph + 53.4 Gas=1

48 cases used, 3 cases contain missing values

Predictor Coef SE Coef T P VIF

Constant -411.4 103.1 -3.99 0.000

N/V 15.561 4.193 3.71 0.001 3.340

Accel+TRLHP_50 mph 3.5481 0.2778 12.77 0.000 1.108

Gas=1 53.36 30.99 1.72 0.092 3.312

13 Date: May 20, 2011

S = 53.6231 R-Sq = 86.7% R-Sq(adj) = 85.8%

Analysis of Variance

Source DF SS MS F P

Regression 3 824160 274720 95.54 0.000

Residual Error 44 126519 2875

Total 47 950679

-150 -100 -50 0 50 100

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI9

Percent

Mean -2.14347E-13

StDev 51.88

N 48

AD 0.631

P-Value 0.094

Probability Plot of RESI9

Normal

Model evaluation chart

Residuals NOT

well-behaved
[10] ln(CO2

) Model – with Fuel indicator variable

The regression equation is

ln CO2 = 4.92 + 0.0253 N/V + 0.00481 Accel+TRLHP_50 mph + 0.0469 Gas=1

48 cases used, 3 cases contain missing values

Predictor Coef SE Coef T P VIF

Constant 4.9200 0.1436 34.25 0.000

N/V 0.025282 0.005839 4.33 0.000 3.340

Accel+TRLHP_50 mph 0.0048132 0.0003869 12.44 0.000 1.108

Gas=1 0.04686 0.04315 1.09 0.283 3.312

14 Date: May 20, 2011

S = 0.0746721 R-Sq = 86.6% R-Sq(adj) = 85.7%

Analysis of Variance

Source DF SS MS F P

Regression 3 1.58430 0.52810 94.71 0.000

Residual Error 44 0.24534 0.00558

Total 47 1.82964

-0.2 -0.1 0.0 0.1 0.2

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI10

Percent

Mean 4.996004E-16

StDev 0.07225

N 48

AD 0.392

P-Value 0.365

Probability Plot of RESI10

Normal

Model evaluation chart

Residuals

well-behaved
[11] CO2 Model – Model [7] + Fuel indicator variable

The regression equation is

CO2 = - 238 + 12.0 N/V + 11.9 Total Cycle Power (Hp) + 26.8 Gas=1

48 cases used, 3 cases contain missing values

Predictor Coef SE Coef T P VIF

Constant -237.67 92.80 -2.56 0.014

N/V 11.997 3.901 3.08 0.004 3.471

Total Cycle Power (Hp) 11.8654 0.8294 14.31 0.000 1.166

Gas=1 26.80 27.75 0.97 0.339 3.190

15 Date: May 20, 2011

S = 48.9358 R-Sq = 88.9% R-Sq(adj) = 88.2%

Analysis of Variance

Source DF SS MS F P

Regression 3 845312 281771 117.66 0.000

Residual Error 44 105367 2395

Total 47 950679

-100 -50 0 50 100

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI11

Percent

Mean -2.52243E-13

StDev 47.35

N 48

AD 1.070

P-Value 0.008

Probability Plot of RESI11

Normal

Model evaluation chart

Residuals NOT

well-behaved
[12] ln(CO2

) Model – Model [8] + Fuel indicator variable

The regression equation is

ln CO2 = 5.16 + 0.0211 N/V + 0.0157 Total Cycle Power (Hp) + 0.0081 Gas=1

48 cases used, 3 cases contain missing values

Predictor Coef SE Coef T P VIF

Constant 5.1573 0.1419 36.35 0.000

N/V 0.021052 0.005964 3.53 0.001 3.471

Total Cycle Power (Hp) 0.015736 0.001268 12.41 0.000 1.166

Gas=1 0.00814 0.04243 0.19 0.849 3.190

16 Date: May 20, 2011

S = 0.0748223 R-Sq = 86.5% R-Sq(adj) = 85.6%

Analysis of Variance

Source DF SS MS F P

Regression 3 1.58331 0.52777 94.27 0.000

Residual Error 44 0.24633 0.00560

Total 47 1.82964

-0.2 -0.1 0.0 0.1 0.2

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI12

Percent

Mean 2.960595E-16

StDev 0.07239

N 48

AD 0.834

P-Value 0.029

Probability Plot of RESI12

Normal

Model evaluation chart

Residuals NOT

well-behaved
[13] CO2 Model – Coastdown terms + N/V + ALVW

The regression equation is

CO2 = - 234 + 3.02 F0 + 29.9 F1 + 2195 F2 + 12.6 N/V + 0.0202 ALVW (test weight)

48 cases used, 3 cases contain missing values

Predictor Coef SE Coef T P VIF

Constant -234.25 78.88 -2.97 0.005

F0 3.0208 0.6759 4.47 0.000 3.679

F1 29.87 25.66 1.16 0.251 1.703

F2 2195.2 266.0 8.25 0.000 1.649

N/V 12.550 2.262 5.55 0.000 1.696

Perhaps why drive type 

(e.g. 4x4) was not 

statistically significant

17 Date: May 20, 2011

N/V 12.550 2.262 5.55 0.000 1.696

ALVW (test weight) 0.020197 0.007671 2.63 0.012 3.956

S = 40.5908 R-Sq = 92.7% R-Sq(adj) = 91.9%

Analysis of Variance

Source DF SS MS F P

Regression 5 881479 176296 107.00 0.000

Residual Error 42 69200 1648

Total 47 950679

-100 -50 0 50 100

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI13

Percent

Mean -2.39216E-13

StDev 38.37

N 48

AD 0.754

P-Value 0.046

Probability Plot of RESI13

Normal

Model evaluation chart

Residuals NOT

well-behaved
[14] ln(CO2

) Model – Coastdown terms + N/V + ALVW

The regression equation is

ln CO2 = 5.18 + 0.00510 F0 + 0.0534 F1 + 2.59 F2 + 0.0190 N/V

+ 0.000023 ALVW (test weight)

48 cases used, 3 cases contain missing values

Predictor Coef SE Coef T P VIF

Constant 5.1765 0.1078 48.02 0.000

F0 0.0051013 0.0009238 5.52 0.000 3.679

F1 0.05342 0.03506 1.52 0.135 1.703

Perhaps why drive type 

(e.g. 4x4) was not 

statistically significant

18 Date: May 20, 2011

F1 0.05342 0.03506 1.52 0.135 1.703

F2 2.5869 0.3635 7.12 0.000 1.649

N/V 0.018989 0.003091 6.14 0.000 1.696

ALVW (test weight) 0.00002325 0.00001048 2.22 0.032 3.956

S = 0.0554768 R-Sq = 92.9% R-Sq(adj) = 92.1%

Analysis of Variance

Source DF SS MS F P

Regression 5 1.70038 0.34008 110.50 0.000

Residual Error 42 0.12926 0.00308

Total 47 1.82964

-0.10 -0.05 0.00 0.05 0.10 0.15

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI14

Percent

Mean -2.77556E-16

StDev 0.05244

N 48

AD 0.587

P-Value 0.119

Probability Plot of RESI14

Normal

Model evaluation chart

Residuals near

well-behaved
[15] CO2 Model – Coastdown terms + Axle Ratio + ALVW

The regression equation is

CO2 = - 570 + 2.18 F0 + 37.4 F1 + 2257 F2 + 189 Axle Ratio

+ 0.0222 ALVW (test weight)

Predictor Coef SE Coef T P VIF

Constant -569.73 76.85 -7.41 0.000

F0 2.1773 0.3921 5.55 0.000 2.699

F1 37.36 18.54 2.01 0.050 1.621

F2 2257.1 184.3 12.25 0.000 1.416

Axle Ratio 189.47 18.63 10.17 0.000 1.650

19 Date: May 20, 2011

ALVW (test weight) 0.022198 0.004725 4.70 0.000 2.681

S = 30.4499 R-Sq = 95.8% R-Sq(adj) = 95.3%

Analysis of Variance

Source DF SS MS F P

Regression 5 945144 189029 203.87 0.000

Residual Error 45 41724 927

Total 50 986868

-100 -50 0 50

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI15

Percent

Mean 1.616136E-13

StDev 28.89

N 51

AD 0.550

P-Value 0.149

Probability Plot of RESI15

Normal

Model evaluation chart

Residuals

well-behaved
[16] ln(CO2

) Model – Coastdown terms + Axle Ratio + ALVW

The regression equation is

ln CO2 = 4.66 + 0.00348 F0 + 0.0631 F1 + 2.71 F2 + 0.290 Axle Ratio

+ 0.000029 ALVW (test weight)

Predictor Coef SE Coef T P VIF

Constant 4.65906 0.09337 49.90 0.000

F0 0.0034826 0.0004763 7.31 0.000 2.699

F1 0.06310 0.02253 2.80 0.007 1.621

F2 2.7147 0.2239 12.13 0.000 1.416

Axle Ratio 0.28956 0.02264 12.79 0.000 1.650

20 Date: May 20, 2011

ALVW (test weight) 0.00002887 0.00000574 5.03 0.000 2.681

S = 0.0369938 R-Sq = 96.8% R-Sq(adj) = 96.4%

Analysis of Variance

Source DF SS MS F P

Regression 5 1.84263 0.36853 269.28 0.000

Residual Error 45 0.06158 0.00137

Total 50 1.90421

-0.10 -0.05 0.00 0.05 0.10

99

95

90

80

70

60

50

40

30

20

10

5

1

RESI16

Percent

Mean -1.18424E-15

StDev 0.03510

N 51

AD 0.283

P-Value 0.621

Probability Plot of RESI16

Normal

Model evaluation chart

Residuals

well-behaved
[16] ln(CO2

) Model – Coastdown terms + Axle Ratio + ALVW

7.0

6.9

6.8

S 0.0354518

R-Sq 96.8%

R-Sq(adj) 96.7%

Regression

90% PI

1100

1000

900

C O 2

Low er 90% PI

Upper 90% PI

Variable

90% Prediction Intervals for ln(CO2) 90% Prediction Intervals for CO2

21 Date: May 20, 2011

6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7.0

6.7

6.6

6.5

6.4

6.3

6.2

6.1

ln(CO2) Fits from Model [16]

ln(CO2)

400 500 600 700 800 900 1000 1100

900

800

700

600

500

400

CO2 Fits from Model [16]

CO2 Actual
Application of Model to Analytical Adjustment

Best Candidate Model:

CO2 = - 570 + 2.18 F0 + 37.4 F1 + 2257 F2 + 189 

Axle Ratio + 0.0222 ALVW (test weight)

Subscript [1]  Input for Original data point

22 Date: May 20, 2011

Subscript [1]  Input for Original data point

Subscript [2]  Input for Data Point to be determined 

Analytically

CO2[2] = CO2[1] + 2.18(F0[2]-F0[1]) 

+ 37.4(F1[2]-F1[1]) + 2257(F2[2]-F2[1]) 

+ 189(AR[2]-AR[1])+ 0.0222(ALVW[2]-ALVW[1])

New for June 28, 2011
Additional Requested Analysis –

Diesel vs. Gas

 Sufficient data was not available to generate 

separate accurate and statistically well 

behaved models for Diesel and gas

 This question should be revisited as more 

data becomes available

23 Date: May 20, 2011

 A Diesel versus gas indicator was tested as a 

potential input to the combined model, but did 

not improve the model

 Likely because other inputs already being 

used (axle ratio, ALVW) correlate well and 

work as a surrogate for Diesel/Gas

New for June 28, 2011
Additional Requested Analysis – City 

and Highway

 Separate models were generated for city and 

highway

 Both models fit the data well and are statistically well 

behaved, particularly for the ln(CO2)

 Results appear on next two slides

24 Date: May 20, 2011

 Results appear on next two slides

 While these models are useful for development 

purposes, Ford recommends use of single combined 

city/highway model for purposes of AD CO2

 Aligns with exact regulatory requirement for input to 

fleet average calculation

 Avoids added variability associated with combining 

results of two models with associated uncertainties

New for June 28, 2011
EPA City CO2 Model

The regression equation is

EPA City ln(CO2) = 5.04 + 0.000041 ALVW + 0.247 Axle Ratio + 0.00324 F0 + 0 F1 + 1.03 F2

Predictor Coef SE Coef T P VIF

Constant 5.04190 0.09970 50.57 0.000

ALVW (test weight) 0.00004090 0.00000577 7.09 0.000 2.084

Axle Ratio 0.24715 0.02519 9.81 0.000 1.574

F0 0.0032426 0.0005204 6.23 0.000 2.482

F2 1.0307 0.2444 4.22 0.000 1.300

S = 0.0421408 R-Sq = 94.3% R-Sq(adj) = 93.8%

FittedLine Plot

F1 coefficient not 

statistically significant 

so set to 0.

25 Date: May 20, 2011

Analysis of Variance

Source DF SS MS F P

Regression 4 1.34536 0.33634 189.40 0.000

Residual Error 46 0.08169 0.00178

Total 50 1.42705

Source DF Seq SS

ALVW (test weight) 1 0.68374

Axle Ratio 1 0.56211

F0 1 0.06792

F2 1 0.03158

6.3 6.4 6.5 6.6 6.7 6.8 6.9 7.0 7.1

7.1

7.0

6.9

6.8

6.7

6.6

6.5

6.4

6.3

City Model Fitted Values ln(CO2)

EPA

City ln(C

O2)

S 0.0408304

R-Sq 94.3%

R-Sq(adj) 94.2%

FittedLine Plot

EPA City ln(CO2) = - 0.0000 +1.000 FITS4

New for June 28, 2011
The regression equation is

EPA Hwy ln(CO2) = 4.06 + 0.000015 ALVW + 0.363 Axle Ratio + 0.00358 F0 + 0.128 F1 + 5.34 F2

Predictor Coef SE Coef T P VIF

Constant 4.0582 0.1123 36.14 0.000

ALVW (test weight) 0.00001470 0.00000690 2.13 0.039 2.681

Axle Ratio 0.36266 0.02723 13.32 0.000 1.650

F0 0.0035836 0.0005728 6.26 0.000 2.699

F1 0.12846 0.02709 4.74 0.000 1.621

F2 5.3381 0.2692 19.83 0.000 1.416

S = 0.0444883 R-Sq = 97.2% R-Sq(adj) = 96.9%

EPA Highway CO2 Model

FittedLine Plot

26 Date: May 20, 2011

S = 0.0444883 R-Sq = 97.2% R-Sq(adj) = 96.9%

Analysis of Variance

Source DF SS MS F P

Regression 5 3.13995 0.62799 317.29 0.000

Residual Error 45 0.08906 0.00198

Total 50 3.22901

Source DF Seq SS

ALVW (test weight) 1 0.83867

Axle Ratio 1 1.47374

F0 1 0.04766

F1 1 0.00181

F2 1 0.77808

6.0 6.2 6.4 6.6 6.8

6.8

6.6

6.4

6.2

6.0

Hwy Model Fitted Values ln(C02)

EPA

H

wy ln(C

O2)

S 0.0426338

R-Sq 97.2%

R-Sq(adj) 97.2%

FittedLine Plot

EPA Hwy ln(CO2) = 0.0000 +1.000 FITS6

New for June 28, 2011