you are not going to use the log transformation! Using the 'Baseball Salary Data', do the following: (a) Fit a linear regression model with "salary" as the response (and not log(salary), as done in class/lab sessions) and the other 16 variables (excluding "names") as the predictors. (b) What percentage of the variation in salaries is explained by the linear model above? (c) Comment on the coefficient of the predictor "hits". Is this coefficient consistent with what your intuition says should be the relationship between number of hits and salary? Why or why not? (d) Test the null hypothesis (using level of significance a = 0.05) that none of the 16 predictors is related to salary. What is the proper conclusion about the linear model above and its utility? (e) Test the null hypothesis (using level of significance a = 0.05) that the variables "batting average", "on base percentage", "hits", "doubles" and "triples" are not needed in the same model with the other 11 predictors. Is the result surprising? Give a possible explanation for the result. (f) What percentage of the variation in salaries is explained by the linear model containing the 11 variables not named in part (e) above? (g) Obtain residuals from the linear model fitted in part (f) above, and produce the following three plots: (i) the residuals versus the predicted values, (ii) a kernel density estimate of the residuals, and (iii) a Normal probability (or Q-Q) plot of the standardized residuals. Comment on the plots