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A consumer group rates 54 cars on Cost/Mile, a Road-Test score given by an expert driver, Predicted Reliability based on historical repair records for the cars, and a Value Score given by owners of the cars. Above is multiple regression output (with deletions) from Minitab relating these variables. Use it to answer Questions 19-24. Regression Analysis: Value Score versus Cost/Mile, Road-Test Score, Predicted Reliability Model Summary R-sq R-sq(adj) S 0.0721260 T-Value 13.42 P-Value 0.000 Coefficients Term Constant Cost/Mile Road-Test Score Predicted Reliability Coef 1.2444 -2.043 0.01138 0.1651 SE Coef 0.0927 0.105 0.00123 0.0102 9.25 0.000 19. The variance of the model residuals is 0.00491. The variance of Value Score is 0.0726. Based on these, the R-squared for this model is: A. 93.2% B. 6.8% C. 87.2% D. 12.8% E. Cannot be computed based on the given information. 20. The T-Value for Predicted Reliability is: A. 12.53 B. 16.26 C. 4.170 D. 1.626 E. Cannot be computed based on the given information.
21. The P-Value for the Cost/Mile ($1000) variable is: A. > 0.10 B. Between 0.05 and 0.10 C. Between 0.01 and 0.05 D. < 0.01 22. Another variable that was recorded is Price. It has a correlation of 0.945 with Cost/Mile. Adding Price to this model: A. Must increase the adjusted R-squared. B. Must decrease the multiple R-squared. C. Should not be done. D. Both B and C. E. None of the above.
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