Question 27 - 33: To get a clearer picture of what affects the number of hits a pitcher gets, the data set for the 57 pitchers from questions 13 - 26 was expanded to include the following explanatory variables: IP = innings pitched HB = hit batters (batters hit by a pitch) BB = base on balls (the number of walks issues by a pitcher) SO = strikeouts The computer output for both the regression and the ANOVA table are given below.

Regression output Coefficients Standard Error t Stat P-value Intercept 90.802 28.151 3.226 0.002 IP 0.840 0.165 5.089 0.000 HB 0.101 0.509 0.200 0.843 BB -0.375 0.143 -2.626 0.011 SO -0.341 0.061 -5.570 0.000 ANOVA of SS MS F p-value Regression 4 9808.487 2452.122 10.982 0.000 Residual 52 11611.233 223.293 Total 56 21419.719Questions 13 - 16: Data was gathered from 57 pitchers in Major League Baseball in the 2018-2019 season to explore whether the number of strikeouts a pitcher gets is an effective predictor for the number of hits they give up. Here is the associated ANOVA table. 55 Regression 1653.982 4602 0036 Residual 19765.737 ' ' Total 21419.719 13. Based on what you see here, can you determine number of strikeouts is an effective predictor of number of hits? Why? 14-. What percent of variability in hits in explained by strikeouts? Write your answer as a percent rounded to two decimal places (i.e. in the form XX.XX%) 15. Calculate the standard deviation of errors. Round your answer to three decimal places. 16. The standard deviation of the observed number of strikeouts is 41.24. Use this information to help calculate the standard error for the slope. Round your answer to three decimal places