automobile design

Automobile design

Basic Econometrics Case

Anshat Singhal B09070

Background

• Automobile designers worried about the gasoline mileage a car would give

• Would it result in violation of Corporate Average fuel Efficiency regulations

• Weight of the car was the main concern• The kind of factors that would affect were like a BLACK BOX to

them• The Goal is to get an equation and predict the mileage.

Overview

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Lets begin…

GPM City= .00943234+ .00001 Weight (lb)

Change of scale of parameters

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GP1000M City= 9.43234+ .01362 Weight (lb)

Residuals

Analysis

GP1000M City= 9.43234+ .01362 Weight (lb)

For 4000 lb => 63.8 GP1000M

By 95% confidence interval => 55.3-72.5 GP1000M

Cost at $1.2 per gallon => 66.36 – 87.00 $/1000M

Correlations

Scatter plot

Do not forget the power

GP1000M City= 11.7 + .0089 Weight (lb) +.088 HorsepowerR2 from 77% to 84 %Horse power capture one third of residual variation with t statistic = 7.29

Scatter plot/ Residual plot

Discreetness of responseskewness in the residual has reduced

Weight v. Horsepower

High Correlation

Weight is the real problem

HP/Pound by weight

• T statistic of weight is higher• R2 is good at 84%

Variation Explained

Final..

Conclusion

• Weight and Horsepower are the important factors

• Power to weight gives the better equation

Prediction Interval• [57.3-71.3]GP1000M=> [14.0-17.5] MPG

Thank you