QUESTION 42 2.326 points Save An antiques dealer is interested in factors that m

Question

QUESTION 42 2.326 points Save An antiques dealer is interested in factors that might influence the final selling price of grandfather clocks at auction. Her data (from 32 previously auctioned clocks) includes the age of the clock (Age), the number of bidders (Bidders) at the auction, and the final selling price (Y)of the clocks. SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Observations 0.944834723 0.892712653 0.885313526 133.1365018 32 ANOVA MS 4277159.703 2138580 120.6511 76907E15 29 514034 5153 17725.33 1 491194 219 Residual Total cients Stondord Error tStat p-value Lower 95% 95% Intercept Age Bidders -1336.722052 173. 3561261-7710841.6TE-08-2691.27514-982, 168%45 12.73619884 0.902380487 14114 1.6E-14 10.89062352 14 58177416 85.8151326 8.705756815 9.857286 9.14E-1 68.00886071 103.6204045 What would the conclusion be, in the context of the problem, of the full model F test at the 0.05 level of significance? OReject the null hypothesis and conclude that the coefficients for Age, Bidders. and the intercept term are not equal to 0 (i.e., all beta terms are not equal to 0). OFail to reject the null hypothesis. We cannot conclude that any of the coefficients O Fail to reject the null hypothesis. We conclude that Age, Bidders, and sale price O Reject the null hypothesis and conclude that the beta coefficients for both Age are not equal to 0. and Bidders are not equal to 0. Reject the null hypothesis and conclude that the beta coefficients for either Age, or Bidders, or both are not equal to 0 (ie, at least one coefficient is not equal to Fail to reject the alternative hypothesis. We conclude that Age- Bidders.

Explanation / Answer

1.
Correct answer: option (A)
Here P-value of regression is 8.76907E-15 < alhpa 0.05 so we reject H0
i.e. the regression equation is best fit to the given data
i.e. all coefficients are not equal to 0 i.e. all coefficient are significant

2.

Correct answer: option (C)
since
R-squared measures the proportion of the variation in your dependent variable (Y) explained by your independent variables (X) for a linear regression model. Adjusted R-squared adjusts the statistic based on the number of independent variables in the model.

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