1. Automobiledesign BasicEconometricsCase AnshatSinghalB09070

2. 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.

3. Overview Text area

4. Lets begin… GPM City= .00943234+ .00001 Weight (lb)

5. Change of scale of parameters Text area GP1000M City= 9.43234+ .01362 Weight (lb)

6. Residuals

7. Analysis GP1000MCity= 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

8. Correlations

9. Scatter plot

10. Do not forget the power GP1000M City= 11.7 + .0089 Weight (lb) +.088 Horsepower R2 from 77% to 84 % Horse power capture one third of residual variation with t statistic = 7.29

11. Scatter plot/ Residual plot Discreetness of response skewnessin the residual has reduced

12. Weight v. Horsepower High Correlation

13. Weight is the real problem

14. HP/Pound by weight T statistic of weight is higher R2 is good at 84%

15. Variation Explained

16. Final..

17. Conclusion Weightand Horsepower are the important factors Powerto weight gives the better equation Prediction Interval [57.3-71.3]GP1000M=> [14.0-17.5] MPG

18. Thank you