6. (15 points) In order to adjust for gender, height and age, multiple regression was used to build a second model. The output from this multivariate model is below. > summary(lm(Cholesterol-., temp)) Call: lm(formula = Cholesterol ~ ., data = temp) Residuals: Min 10 Median 30 Max -56.60 -13.15 -2.21 9.92 111.31 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 459.740 62.008 -1.12 0.26355 Age 1.651 0.379 4.36 3.3e-05 m Gender -7.975 7.934 -1.01 0.31739 Weight -0.318 0.119 -2.68 0.00858 ** Height 3.168 0.886 3.58 0.00055 *** Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 28 on 95 degrees of freedom Multiple R-squared 0.271, Adjusted R-squared: 0.24 F-statistic: 8.81 on 4 and 95 DF,p-value: 4.27e-06 Based on this model: a. What percentage of the variability in cholesterol is explained by its linear relationship with age, gender, height and weight? b. What is the predicted difference in cholesterol between two men who are one pound different in weight, but the same height and age? 0. According to this model, is there a signicant linear relationship between weight and cholesterol, after adjusting for gender, height and age