We consider the data set on baseball players in the Major League. The data set contains the following variables: Salary: 1987 annual salary on opening day in thousands of dollars Hits: Number of hits in 1986 HmRun: Number of home runs in 1986 Runs: Number of runs in 1986 RBI: Number of runs batted in in 1986 Walks: Number of walks in 1986 . Years: Number of years in the major leagues League: A factor variable with A and N indicating player's league at the end of 1986 (American or National League) Summary statistics of the data is reported below . League A:109 N: 91 Salary Min. 67.5 1st Qu.: 193.8 Median : 440.8 Mean : 552.0 3rd Qu.: 750.0 Max : 2460.0 Hits Min 1.00 1st Qu. : 71.75 Median : 101.50 Mean : 106.07 3rd Qu. : 139.50 Max :238.00 HmRun Min. : 0.00 1st Qu.: 5.00 Median : 10.00 Mean : 12.47 3rd Qu.: 19.00 Max. : 40.00 RBI Min. : 0.00 1st Qu.: 31.00 Median : 47.50 Mean :52.95 3rd Qu.: 74.00 . : 121.00 Walks Min. : 0.00 1st Qu.:23.00 Median :36.50 Mean :41.05 3rd Qu.: 58.00 Max. :97.00 Years Min. : 1.00 1st Qu.: 4.00 Median 6.00 Mean : 7.53 3rd Qu.: 11.00 Max : 24.00 1. Suppose that you create a box plot of each variable in this data set. Answer which variable has the widest box and provide its box length (2 decimal places). using only numbers in the summary statistics above. 2. We obtain an estimation result as below. Explain the effect of Hits on Salary in dollars (no more than 30 words). glm (formula = Salary data = DATA) Deviance Residuals: Min 1Q Median -694.89 -214.03 -54.77 3Q Max 106.34 2309.68 Coefficients: Estimate Std. Error t value Pr>t) (Intercept) -295.211 87.288 -3.382 0.000871 *** Hits 2.777 1.221 2.274 0.024080 * HmRun -1.670 6.481 -0.258 0.796959 RBI 2.128 3.058 0.696 0.487340 Walks 4.830 1.603 3.012 0.002939 ** LeagueN 45.888 54.818 0.837 0.403573 Years 32.102 5.547 5.787 2.85e-08 *** Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 0.1 1 3. We obtain an estimation result as below. Explain the effect of Hits on Salary no more than 30 words). glm(formula = log (Salary) data = DATA) Deviance Residuals: Min 1Q Median -1.6369 -0.4602 0.0076 3Q 0.4355 Max 3.3260 Coefficients: Estimate Std. Error t value Pr>t]) (Intercept) 4.019310 0.145199 27.681 t) (Intercept) 3.880068 0.185398 20.928 t) (Intercept) -295.211 87.288 -3.382 0.000871 *** Hits 2.777 1.221 2.274 0.024080 * HmRun -1.670 6.481 -0.258 0.796959 RBI 2.128 3.058 0.696 0.487340 Walks 4.830 1.603 3.012 0.002939 ** LeagueN 45.888 54.818 0.837 0.403573 Years 32.102 5.547 5.787 2.85e-08 *** Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 0.1 1 3. We obtain an estimation result as below. Explain the effect of Hits on Salary no more than 30 words). glm(formula = log (Salary) data = DATA) Deviance Residuals: Min 1Q Median -1.6369 -0.4602 0.0076 3Q 0.4355 Max 3.3260 Coefficients: Estimate Std. Error t value Pr>t]) (Intercept) 4.019310 0.145199 27.681 t) (Intercept) 3.880068 0.185398 20.928