#7 Use forward regression analysis to obtain a model in which the predictor vari
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Question
#7 Use forward regression analysis to obtain a model in which the predictor variables contribute to the prediction of AskPrice. Use a 10% significance level. (Use data on subsequent pages)
Variable Description:
AskPrice: Asking price of a used Camaro
Age: Car’s age in years
Mileage: Car’s mileage in thousands of miles
ConditionAverage: Dummy variable equal to 1 for average condition and 0 if not
ConditionPoor: Dummy variable equal to 1 if poor condition and 0 if not
Dealer_Indiv: Dummy variable equal to 1 if dealer sold car and 0 if seller is an individual
SUMMARY OUTPUT #1 Regression Statistics 0.872438426 0.761148808 Coefficients Standard Error 17291.60548 1037.504246 16.66653948 2.15289E-16 15169.66988 19413.54107 -1909.218465198.6028828 -9.613246484 1.60256E-10 -2315.407 189 1503.02974 Multiple R Square t Stat P-value Lower 95% Intercept Rearession Statistics Multiple R R Square 0.8589931 0.737869145 CoefficientsStandard Error 16270.90802 997.519026816.31137611 3.79314E-16 14230.7514318311.06462 150.4673058 16.65376922-9.035030074 6.26139E-10184.5281068-116.4065047 t Stat P-value Lower 95% Intercept Mileacge Regression Statistics Multiple R 0.076707742 0.005884078 CoefficientsStandard Error 8264.863636 947.0427728 8.7270225531.3175E-09 6327.942628 10201.78464 728.1969697 t Stat P-value Lower 95% Intercept 1757.637.414304529 0.6816996554322.970224 2866.576284 Regression Statistics Multiple R R Square 0.527115278 0.277850516 Coefficients Standard Error t Stat P-vajue Lower 95% Upper 95% 10252.6875 946.4898936 10.83232644 1.04374E-11 8316.89725712188.47774 Intercept ConditionPoor 4545.08751360.664952 -3.340342891 0.0023125987327.961311-1762.213689 Rearession Statistics 0.658768182 0.433975517 Coefficients Standard Error 5641.055556790.0303497 7.140302341 7.38518E-08 4025.261184 7256.849927 5752.636752 219,98066 4.715350774 5.59274E-05 3257.494781 8247.778723 Multiple R t Stat p-value | Lower 95% Upper 95% ntercept Dealer IndivExplanation / Answer
Decision rule : If p value < 0.1, then predictor variable is significant relation with Price
(Provided only for first page)
Variable P value Decision Age 0.00000000016 < 0.1 significant mileage 0.000000000626 < 0.1 significant Condition average 0.6817 > 0.1 Not significant Condition poor 0.00147 < 0.1 Significant Dealer_ ind 0.000055927 < 0.1 SignificantRelated Questions
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