Your colleague prepared a multiple regression analysis to predict computer prices and feels very good since the R-square is 0.9732. She shares her model seeking your opinion. SUMMARY OUTPUT Regression Statistics Multiple R 0.98651 R Square 0.97320 Adjusted R Square 0.95176 Standard Error 135.79027 Observations 10 ANOVA df Significance F 0.00040 45.39028 Regression Residual Total SS MS 4 3347805.01047 836951.25262 5 92194.98953 18438.99791 9 3440000 Upper 95% 1697.0487 22.0856 17.6122 0.1093 3.4687 Lower 95.0% 24.6053 -32.7899 -11.4515 -0.3647 1.2928 Coefficients Standard Error Stat P-value Lower 95% Intercept 860.8270 325.3044 2.6462 0.0456 24.6053 Comp. Power -5.3522 10.6737 -0.5014 0.6374 -32.7899 RAM 3.0804 5.6531 0.5449 0.6092 -11.4515 Storage -0.1277 0.0922 -1.3848 0.2247 -0.3647 Chip Price 2.3808 0.4232 5.6252 0.0025 1.2928 After reviewing the regression output, which would be the correct feedback to your colleague? O a. The model is excellent and meets all the regression assumptions and statistical tests. O b. Based on the F-Test, we should reject the model since Significant F is almost zero. O c. Several of the p-values are above 0.05 and therefore we should reject this model. d. Chip price has a p-value less than 0.05 and therefore should be removed from the model. O e. None of the above