Draw a scatter plot of the residuals for each line of best fit for Pick Number vs Career Length and for Pick Number vs Salary Hint: We want to get the predictions for every player in the dataset Hint 2: This question is really involved, try to follow the skeleton code! n [36]: icted_career_lengths = ... icted_salaries = ... er_length_residuals = ... ry_residuals = ... with_residuals = nfl.with_columns("Career Length Residuals", career_length_residuals, "Salary Residuals", salary_residu with_residuals.show(5) w generate two scatter plots! with_residuals.scatter("Pick Number", "Career Length Residuals") with_residuals.scatter( "Pick Number", "Salary Residuals") Player Salary Year Drafted Pick Number Position Career Length Career Length Residuals Salary Residuals Baker Mayfield 570000 2018 1 1 QB 2 Ellipsis Ellipsis Cam Newton 16200000 2011 1 QB 9 Ellipsis Ellipsis Eli Manning 11500000 2004 1 QB 16 Ellipsis Ellipsis Eric Fisher 10350000 2013 1 OT 7 Ellipsis Ellipsis Jadeveon Clowney 15967200 2014 1 DE 6 Ellipsis Ellipsis Question 2.5 Assign career_length_residual_corr and salary_residual_corr to either 1, 2 or 3 corresponding to whether or not the correlation between Pick Number and Career Length Residuals is positive, zero, or negative, and to whether or not the correlation between Pick Number and Salary Residuals is positive, zero, or negative respectively. 1. Positive 2. Zero 3. Negative career_length_residual_corr salary_residual_corr =