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Automobiledesign<br />BasicEconometricsCase<br />AnshatSinghalB09070<br />
Background<br />Automobile designers worried about the gasoline mileage a car would give<br />Would it result in violation...
Overview<br />Text area<br />
Lets begin…<br />GPM City= .00943234+ .00001 Weight (lb)<br />
Change of scale of parameters<br />Text area<br />GP1000M City= 9.43234+ .01362 Weight (lb)<br />
Residuals<br />
Analysis<br />GP1000MCity= 9.43234+ .01362 Weight (lb)<br />For 4000 lb => 63.8 GP1000M<br />By 95% confidence interval =>...
Correlations<br />
Scatter plot<br />
Do not forget the power<br />GP1000M City= 11.7 + .0089 Weight (lb) +.088 Horsepower<br />R2 from 77% to 84 %<br />Horse p...
Scatter plot/ Residual plot<br />Discreetness of response<br />skewnessin the residual has reduced<br />
Weight v. Horsepower<br />High Correlation<br />
Weight is the real problem<br />
HP/Pound by weight<br />T statistic of weight is higher<br />R2 is good at 84%<br />
Variation Explained<br />
Final..<br />
Conclusion<br />Weightand Horsepower are the important factors<br />Powerto weight gives the better equation<br />Predicti...
Thank you <br />
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Automobile design

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Transcript of "Automobile design"

  1. 1. Automobiledesign<br />BasicEconometricsCase<br />AnshatSinghalB09070<br />
  2. 2. Background<br />Automobile designers worried about the gasoline mileage a car would give<br />Would it result in violation of Corporate Average fuel Efficiency regulations<br />Weight of the car was the main concern<br />The kind of factors that would affect were like a BLACK BOX to them<br />The Goal is to get an equation and predict the mileage.<br />
  3. 3. Overview<br />Text area<br />
  4. 4. Lets begin…<br />GPM City= .00943234+ .00001 Weight (lb)<br />
  5. 5. Change of scale of parameters<br />Text area<br />GP1000M City= 9.43234+ .01362 Weight (lb)<br />
  6. 6. Residuals<br />
  7. 7. Analysis<br />GP1000MCity= 9.43234+ .01362 Weight (lb)<br />For 4000 lb => 63.8 GP1000M<br />By 95% confidence interval =>55.3-72.5 GP1000M<br />Cost at $1.2 per gallon => 66.36 – 87.00 $/1000M<br />
  8. 8. Correlations<br />
  9. 9. Scatter plot<br />
  10. 10. Do not forget the power<br />GP1000M City= 11.7 + .0089 Weight (lb) +.088 Horsepower<br />R2 from 77% to 84 %<br />Horse power capture one third of residual variation with t statistic = 7.29<br />
  11. 11. Scatter plot/ Residual plot<br />Discreetness of response<br />skewnessin the residual has reduced<br />
  12. 12. Weight v. Horsepower<br />High Correlation<br />
  13. 13. Weight is the real problem<br />
  14. 14. HP/Pound by weight<br />T statistic of weight is higher<br />R2 is good at 84%<br />
  15. 15. Variation Explained<br />
  16. 16. Final..<br />
  17. 17. Conclusion<br />Weightand Horsepower are the important factors<br />Powerto weight gives the better equation<br />Prediction Interval<br />[57.3-71.3]GP1000M=> [14.0-17.5] MPG<br />
  18. 18. Thank you <br />
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