using the provided r code, find the OLR model and Look at the summary() output for the model provided. For each possible predictor variable state whether there is evidence that the true slope coefficient in front of the variable is not equal to 0 using the associated Pvalue in the output. > model=lm(price~lotsize+bedrooms+bathrooms+stories+garage, data=HousePrices) > model Call: lm(formula = price ~ lotsize + bedrooms + bathrooms + stories + garage, data = HousePrices) Coefficients: (Intercept) lotsize bedrooms bathrooms stories garage -2063.640 4.733 2388.488 16148.061 7879.441 5371.953 > summary(model) Call: lm(formula = price ~ lotsize + bedrooms + bathrooms + stories + garage, data = HousePrices) Residuals: Min 1Q Median 3Q Max -48487 -10463 -928 8577 80709 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2063.6401 3521.8874 -0.586 0.5582 lotsize 4.7334 0.3798 12.462 < 2e-16 *** bedrooms 2388.4877 1184.2320 2.017 0.0442 * bathrooms 16148.0605 1695.6955 9.523 < 2e-16 *** stories 7879.4411 981.4634 8.028 6.22e-15 *** garage 5371.9533 953.5204 5.634 2.84e-08 *** --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 17770 on 540 degrees of freedom Multiple R-squared: 0.5613, Adjusted R-squared: 0.5573 F-statistic: 138.2 on 5 and 540 DF, p-value: < 2.2e-16