C7 Use he data in ATTEND for this exercise. (1) in the model of Example 6.3, argue that Astndful/ApriGPA - B. + 2B.priGPA + Buatndrte. Use equation (6.19) to estimate the partial effect when priGPA = 2.59 and aindrte = 82. Interpret your estimate. Copyright 2018 Cepeling. All Rights Reserved. Maybe (ii) Show that the equation can be written as stefnl - 0 + Bandrte + priGPA + B, ACT + B.(priGPA - 2.59) + B.ACT + BopriGPA(atndrte - 32) + u. where 8, = B2 + 2B.2.59) + B.(82) (Note that the intercept has changed, but this is unimportant.) Use this to obtain the standard error of , from part (i). (iii) Suppose that, in place of priGPA(andrte - 82), you put (priGPA - 2.59). (andrte - 82). Now how do you interpret the coefficients on andrte and priGPA? C8 Use the data in HPRICEI for this exercise, (i) Estimate the model price = Be + B,lotsize + B,sqrft + Bybdrms + and report the results in the usual form, including the standard error of the regression. Obtain predicted price, when we plug in lotsize = 10,000, syrft = 2,300, and bdrms = 4; round this price to the nearest dollar. (ii) Run a regression that allows you to put a 95% confidence interval around the predicted value in part (1). Note that your prediction will differ somewhat due to rounding error. (iii) Let price be the unknown future selling price of the house with the characteristics used in parts (i) and (ii). Find a 95% CI for price and comment on the width of this confidence interval. C9 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. (i) 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? (iii) 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.) (iv) 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? (v) Now regress log(wage) on points, exper, exper, age, and coll. Report the results in the usual format (vi) Test whether age and coll are jointly significant in the regression from part (v). What does this imply about whether age and education have separate effects on wage, once productivity and seniority are accounted for? C7 Use he data in ATTEND for this exercise. (1) in the model of Example 6.3, argue that Astndful/ApriGPA - B. + 2B.priGPA + Buatndrte. Use equation (6.19) to estimate the partial effect when priGPA = 2.59 and aindrte = 82. Interpret your estimate. Copyright 2018 Cepeling. All Rights Reserved. Maybe (ii) Show that the equation can be written as stefnl - 0 + Bandrte + priGPA + B, ACT + B.(priGPA - 2.59) + B.ACT + BopriGPA(atndrte - 32) + u. where 8, = B2 + 2B.2.59) + B.(82) (Note that the intercept has changed, but this is unimportant.) Use this to obtain the standard error of , from part (i). (iii) Suppose that, in place of priGPA(andrte - 82), you put (priGPA - 2.59). (andrte - 82). Now how do you interpret the coefficients on andrte and priGPA? C8 Use the data in HPRICEI for this exercise, (i) Estimate the model price = Be + B,lotsize + B,sqrft + Bybdrms + and report the results in the usual form, including the standard error of the regression. Obtain predicted price, when we plug in lotsize = 10,000, syrft = 2,300, and bdrms = 4; round this price to the nearest dollar. (ii) Run a regression that allows you to put a 95% confidence interval around the predicted value in part (1). Note that your prediction will differ somewhat due to rounding error. (iii) Let price be the unknown future selling price of the house with the characteristics used in parts (i) and (ii). Find a 95% CI for price and comment on the width of this confidence interval. C9 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. (i) 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? (iii) 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.) (iv) 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? (v) Now regress log(wage) on points, exper, exper, age, and coll. Report the results in the usual format (vi) Test whether age and coll are jointly significant in the regression from part (v). What does this imply about whether age and education have separate effects on wage, once productivity and seniority are accounted for
