1. Based on the Summary Output produced by the regression analysis write out the linear regression model and explain why you are confident (or not confident) in the model to predict the price.

A B C D E F G H SUMMARY OUTPUT Regression Statistics Multiple R 0.952313 R Square 0.9069 Adjusted R Square 0.90662 Standard Error 0.138905 observations 1000 ANOVA df 55 MS F gnificance F Regression 3 187.1994 62.39979 3234.063 Residual 996 19.21737 0.019295 Total 999 206.4167 Coefficient:andard Err t Stat P-value Lower 95%Upper 95%ower 95.0"pper 95.0% Intercept 0.693086 0.019265 35.97703 2.7E-182 0.655282 0.73089 0.655282 0.73089 cut ord -0.02459 0.00404 -6.08559 1.65E-09 -0.03252 -0.01666 -0.03252 -0.01666 clarity_ord -0.0522 0.002673 -19.5267 3.798-72 -0.05745 -0.04695 -0.05745 -0.04695 price 0.000105 1.15E-06 91.27208 0 0.000103 0.000108 0.000103 0.000108 RESIDUAL OUTPUT Observation edicted car Residuals 1 0.941857 0.288143 2 0.391289 -0.07129 3 0.876051 0.023949 4 1.723984 0.286016 5 0.682051 0.037949 6 0.805795 -0.05579 7 1.128657 0.091343 8 1.554187 -0.04419 9 1.419063 0.100937