The document discusses automobile designers wanting to predict a car's gasoline mileage based on its weight. It presents a linear regression model that found mileage (GP1000M City) can be estimated as 9.43234 + .01362 * weight (in pounds). This predicts 63.8 GP1000M for a 4000 pound car, with a 95% confidence interval of 55.3-72.5 GP1000M. A second model adds horsepower as a variable, improving the R2 from 77% to 84%, showing weight and horsepower are important factors for predicting mileage.

1. AutomobiledesignBasicEconometricsCaseAnshatSinghalB09070

2. BackgroundAutomobile designers worried about the gasoline mileage a car would giveWould it result in violation of Corporate Average fuel Efficiency regulationsWeight of the car was the main concernThe kind of factors that would affect were like a BLACK BOX to themThe Goal is to get an equation and predict the mileage.

3. OverviewText area

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

5. Change of scale of parametersText areaGP1000M City= 9.43234+ .01362 Weight (lb)

6. Residuals

7. AnalysisGP1000MCity= 9.43234+ .01362 Weight (lb)For 4000 lb => 63.8 GP1000MBy 95% confidence interval =>55.3-72.5 GP1000MCost at $1.2 per gallon => 66.36 – 87.00 $/1000M

8. Correlations

9. Scatter plot

10. Do not forget the powerGP1000M 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

11. Scatter plot/ Residual plotDiscreetness of responseskewnessin the residual has reduced

12. Weight v. HorsepowerHigh Correlation

13. Weight is the real problem

14. HP/Pound by weightT statistic of weight is higherR2 is good at 84%

15. Variation Explained

16. Final..

17. ConclusionWeightand Horsepower are the important factorsPowerto weight gives the better equationPrediction Interval[57.3-71.3]GP1000M=> [14.0-17.5] MPG

18. Thank you