Question diveded to three parts, so asked in three different questions: part 1: This problem is inspired by a study of the “gender gap” in earnings in top corporate jobs. The study compares total compensation among top executives in a large set of U.S. public corporations in the 1990s. a) Let Female be an indicator variable that is equal to 1 for females and 0 for males. A regression of the logarithm of earnings onto Female yields ln(Earnings)=6.48-0.44Female+uˆ, SER = 2.65. (0.01) (0.05) i) The estimated coefficient on Female is –0.44. Explain what this value means. ii) The SER is 2.65. Explain what this value means.
Question diveded to three parts, so asked in three different questions: part 1: This problem is inspired by a study of the “gender gap” in earnings in top corporate jobs. The study compares total compensation among top executives in a large set of U.S. public corporations in the 1990s. a) Let Female be an indicator variable that is equal to 1 for females and 0 for males. A regression of the logarithm of earnings onto Female yields ln(Earnings)=6.48-0.44Female+uˆ, SER = 2.65. (0.01) (0.05) i) The estimated coefficient on Female is –0.44. Explain what this value means. ii) The SER is 2.65. Explain what this value means.
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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Question diveded to three parts, so asked in three different questions:
part 1:
This problem is inspired by a study of the “gender gap” in earnings in top corporate jobs. The study compares total compensation among top executives in a large set of U.S. public corporations in the 1990s.
a) Let Female be an indicator variable that is equal to 1 for females and 0 for males. A
regression of the logarithm of earnings onto Female yields ln(Earnings)=6.48-0.44Female+uˆ, SER = 2.65.
(0.01) (0.05)
i) The estimated coefficient on Female is –0.44. Explain what this value means.
ii) The SER is 2.65. Explain what this value means.
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