The data set NBASAL contains salary information and career statistics for 269 players in the National Basketball Association (NBA). (1) Estimate a model relating points-per-game (points) to years in the league (exper), age, and years played in college (coll). Include a quadratic in exper, the other variables should appear in level form. Report the results in the usual way. (2) Holding college years and age fixed, at what value of experience does the next year of experi- ence actually reduce points-per-game? Does this make sense? (3) Why do you think coll has a negative and statistically significant coefficient? (Hint: NBA players can be drafted before finishing their college careers and even directly out of high school.) (4) Add a quadratic in age to the equation. Is it needed? What does this appear to imply about the effects of age, once experience and education are controlled for? (5) Now regress log(wage) on points, exper, exper', age, and coll. Report the results in the usual format. (6) Estimate a linear regression model relating points per game to experience in the league and position (guard, forward or center). Include experience in quadratic form and use centers as the base group. Report the results in the usual form. (7) Why do you not include all three position dummy variables in part (6)? (8) Holding experience fixed, does a guard score more than a center? How much more? Is the difference statistically significant? (9) Now, add marital status to the equation. Holding position and experience fixed, are married players more productive (based on points per game)? (10) Add interactions of marital status with both experience variables. In this expanded model, is there strong evidence that marital status affects points per game? (11) Estimate the model from part (9) but use assists per game as the dependent variable. Are there any notable differences from part (9)? Discuss