- Problem C In This Problem You Will Use The Baseball Salary Data Csv Found Under The Item Homework 2 In Assignments O 1 (218.54 KiB) Viewed 53 times
I'm trying again. I have enlarged the screenshots of the baseball
salary documents so you can see it better. Zoom in if you can't see
some of the names/numbers. Please upload a neat, written out
response and clearly number the questions as I am very confused on
what to do. Thank you so much for helping me!
Problem C. In this problem, you will use the Baseball-Salary-Data.csv found under the item “Homework 2" in Assignments on Canvas. Further, in Modules, the supplementary note R Commands for Baseball Analysis.pdf provides some basic R commands that you may find useful for this problem, including how to: (a) read the data into R, and (b) use the command lm() when you have a large number of predictors. (i) Fit a linear regression model with “salary'' as the response (not log(salary)) and the other 16 variables (excluding player names) as the predictors. (ii) What percentage of the variation in salaries is explained by the linear model above? a (iii) 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? (iv) 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? (v) 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. (vi) What percentage of the variation in salaries is explained by the linear model containing the 11 variables not named in part (v) above?
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