# Assignment #8 Fitting and Predictions

Assignment #8 Fitting and Predictions

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 vari-

ables 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 experience 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. Obtain the fitted values from the regression in part (i). What is the range of fit-

ted values? How does it compare with the range of the actual data on math4?

5. Obtain the residuals from the regression in part (i). Which player has the largest (positive) residual? Provide an interpretation

of this residual.

6. Add a quadratic in age to the equation. Is it statistically significant at the 1% level?

It is needed? What does this appear

to imply about the effects of age, once experience and education are controlled

for?

7. Now regress log(wage) on points, exper, exper2, age, and coll. Report the results

in the usual format.

8. Test whether age and coll  are jointly significant in the regression above.

What does this imply about whether age and education have separate effects on wage, once productivity and seniority are accounted for?

9.  Regress wage  regress instead of log(wage) on the same control variables. Decide which model you prefer, why?

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