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Analyzing CO2 Emissions: Vehicle Class, Fuel Type, and More

School
University of Michigan**We aren't endorsed by this school
Course
STATS 413/500
Subject
Industrial Engineering
Date
Dec 11, 2024
Pages
5
Uploaded by KidFog18853
call: Im(formula = CO2.Emissions.g.km. ~ Vehicle_Class + Fuel_Type + Transmission_Numeric + Engine.Size.L. + Fuel.Consumption.Comb..L.100.km., data = co2_data) Residuals: Min 1Q Median 3Q Max -99.648 -8.017 -0.104 9.021 98.284 Coefficients: Estimate Std. Error t value Pr(>|tl) (Intercept) 8.2701 1.8825 4.393 1.14e-05 *** Vehicle_Class 1.2053 0.2626 4.590 4.52e-06 *** Fuel_Type 10.9143 0.2820 38.710 < 2e-16 *** Transmission_Numeric -0.3390 0.6684 -0.507 0.612 Engine.Size.L. 10.3893 0.3217 32.297 < 2e-16 *¥** Fuel.Consumption.Comb. .L.100.km. 14.6587 0.1491 98.282 < 2e-16 *¥** Signif. codes: 0 “***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 * ’ 1 Residual standard error: 18.99 on 6276 degrees of freedom Multiple R-squared: 0.8975, Adjusted R-squared: 0.8974 F-statistic: 1.099e+04 on 5 and 6276 DF, p-value: < 2.2e-16
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QQ Plot Do Theoretical Quantiles 0 | -2 L ; T T T T T -100 -50 0 50 100 Sample Quantiles Residual vs the predicted response
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ool sjenpisay 004~ 450 400 350 300 250 200 150 Fitted values
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Residuals vs Fitted Q-Q Residuals 8 (O - = > 78@H5© © or_ Q b= o) b4 g = T o ° : 5 SR @ w0 - © [} C 8 o w ? - 03818 T T T T T T T 150 200 250 300 350 400 450 4 Fitted values Theoretical Quantiles Scale-Location Residuals vs Leverage O - W o _ » ® 8 o B =} - O ° o g . M o - o = -~ o .2, o g o _| N = ® g = 8 - 3 T e 2 0 o ® & 00 00° 0° 3 pu o 7 (‘t:) < o 126809 © Q 5 ] o o o © - o A T T I T 150 200 250 300 350 400 450 Fitted values Analysis of variance Table 0.004 Leverage Model 1: CO2.Emissions.g.km. ~ Vehicle_Class + Fuel_Type + Transmission_Numeric + Engine.Size.L. Model 2: CO2.Emissions.g.km. ~ Vehicle_Class + Fuel_Type + Transmission_Numeric + Engine.Size.L. + Fuel.Consumption.Comb..L.100.km. Res.Df RSS Df Sum of Sq F Pr(>F) 1 6277 5744953 2 6276 2262588 1 Signif. codes: 0 > > alpha = 0.05 > qf(1-alpha, 1, 6276) [1] 3.842941 3482364 9659.4 < 2.2e-16 *** f¥¥xx’ 0,001 ¥’ 0.01 ‘¢’ 0.05 ‘.7 0.1 Prediction: 1
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Actualvs Predicted Values 700 332 600 00 235 400 182 331 300 233 184 200 100 0 1 2 3 e==CO2 Emission (Actual) =====CO2 Emission (Predicted) call: Im(formula = CO2.Emissions.g.km. ~ Vehicle_Class + Fuel_Type + Transmission_Numeric + Engine.Size.L. + Fuel.Consumption.Comb..L.100.km., data = co2_data) Residuals: Min 1Q Median 3Q Max -99.648 -8.017 -0.104 9.021 98.284 Coefficients: Estimate Std. Error t value Pr(>|tl) (Intercept) 8.2701 1.8825 4.393 1.14e-05 *** Vehicle_Class 1.2053 0.2626 4.590 4.52e-06 *** Fuel_Type 10.9143 0.2820 38.710 < 2e-16 *** Transmission_Numeric -0.3390 0.6684 -0.507 0.612 Engine.Size.L. 10.3893 0.3217 32.297 < 2e-16 *¥** Fuel.Consumption.Comb. .L.100.km. 14.6587 0.1491 98.282 < 2e-16 *¥** Signif. codes: 0 “***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 * ’ 1 Residual standard error: 18.99 on 6276 degrees of freedom Multiple R-squared: 0.8975, Adjusted R-squared: 0.8974 F-statistic: 1.099e+04 on 5 and 6276 DF, p-value: < 2.2e-16
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