Your colleague prepared a multiple regression analysis to predict computer prices and feels very.' good since the Rsquare 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 cit SS MS F Signicance F Regression 4 334780501047 836951.25262 45.39028 0.00040 Residual 5 92194.98953 18438.99791 Total 9 3440000 Coefcients Standard Error t Stat Pvaiue Lower 95% Upper 95% Lower 95. 0% Intercept 860.8270 325.3044 2.6462 0.0456 24.6053 1697.0487 24.6053 Comp. Power 5.3522 10.6737 -0.5014 0.6374 32.7899 22.0856 32.7899 RAM 3.0804 5.6531 0.5449 0.6092 -11.4515 17.6122 -11.4515 Storage 0.1277 0.0922 1.3848 0.2247 0.3647 0.1093 0.3647 Chi Price 2.3808 0.4232 5.6252 0.0025 1.2928 3.468 7 1.2928 After reviewing the regression output, which would be the correct teed back to your colleague? O a. The model is excellent and meets all the regression assumptions and statistical tests. 0 b. Based on the F-Test, we should reject the model since Signicant F is almost zero. c. Several of the p-values are above 0.05 and therefore we should reject this model. C) d. Chip price has a p-value less than 0.05 and therefore should be removed from the model. C) e. None of the above