Statistic and probability

Which team statistic is the best variable to use to predict a team's winning percentage? One pitching, one fielding, and one hitting statistic was measured for each major league baseball team at the end of the 2019 season. The dataset is called "2019 MLB Team Statistics." The three explanatory variables as follows. Earned Run Average (ERA): a pitching statistic Fielding %: a fielding statistic Home Runs: a hitting statistic Win-Loss % Win-Loss Win-Loss 0.6- 0.6 0.6 0.5 0.5 04 0.4 0.4 03 0.3 35 4 4.5 5 55 0.978 0.98 0.982 0.984 0986 0.988 0.3 Earned Run Average Fielding% 150 200 250 300 Home Runs Correlation matrix: Win-Loss % Earned Run Average 0.83399384 Fielding % 0.32183594 Home Runs 0.76281516a. Which explanatory variable has the strongest relationship with the response variable and a reason why in a complete sentence? Interpret the scatterplot of ERA and Win-Loss % in terms of trend, strength, and shape (form) in one complete sentence. Simple linear regression results: Fitted line Dependent Variable: Win-Loss % Win-Loss % Independent Variable: Earned Run Average Win-Loss % = 1.1309735 - 0.1403247 Earned Run Average 0.6 Sample size: 30 R (correlation coefficient) = -0.83399384R-sq = 0.69554573 05 Estimate of error standard deviation: 0.055008616 0.4- 0.3- 3.5 4 4.5 5 5.5 Earned Run Average b. Type the regression equation and Interpret the slope of the regression line (in context of this data set). Is it meaningful to interpret the y- intercept? Why or why not? c. State -squared (i.e., the coefficient of determination) and explain what this value means in context of the data set. d. Use the regression equation from part (c) to predict a random team's Win-Loss % for when their ERA is 7.00. State your predicted value in a sentence that is in context of the data. Is your prediction in part (k) an example of extrapolation? Why or why not