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Automobile design

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Transcript

  • 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

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