automobile design
TRANSCRIPT
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