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1. A researcher is studying the effect of parents' level of education on educational achievement of individuals. Using data on completed years of education (educ), mother's level of education (motheduc), father's level of education (fatheduc), a measure of cognitive ability (abil), and logarithm of family income (L.income), the researcher estimates 4 models. Table (1) shows the OLS estimates for each model, with standard errors in parentheses underneath each coefficient. The dependent variable in all the models is educ. Model 1 Model 2 Model 3 Model 4 Independent variables Lincome (0.2) 0.15 (0.03) 0.10 (0.02) 0.35 (0.03) 0.09 (0.01) 8.74 (0.31) 1210 3541.2 0.49 motheduc 0.27 0.18 (0.03) 0.11 (0.02) 0.52 (0.03) 0.17 (0.03) 0.11 (0.02) 0.39 (0.03) 0.05 (0.01) 8.74 (0.31) 1230 3785.24 0.45 (0.02) fatheduc abil 0.53 (0.03) www abil_squared Intercept N SSR R-squared 6.94 (0.32) 1230 3999.24 0.35 8.4 (0.12) 1230 3899.97 0.38 Ti + 1) a. Interpret the R-squared of the regression in model 1. b. Can you reject the claim that the returns to ability are linear? Be explicit about the hypothesis and any assumptions you make in order to answer the question. c. Carefully interpret the effect of increasing of L.income by 10. d. How the degrees of freedom in model (3) compare to the degrees of freedom in model (4)? Explain. e. Why did the coefficient of motheduc decrease after adding variable fatheduc to the regression? Discuss and if necessary, incorporate the Gauss-Markov assumptions in your answer.

1. A researcher is studying the effect of parents' level of education on educational achievement of individuals. Using data on completed years of education (educ), mother's level of education (motheduc), father's level of education (fatheduc), a measure of cognitive ability (abil), and logarithm of family income (L.income), the researcher estimates 4 models. Table (1) shows the OLS estimates for each model, with standard errors in parentheses underneath each coefficient. The dependent variable in all the models is educ. Model 1 Model 2 Model 3 Model 4 Independent variables Lincome (0.2) 0.15 (0.03) 0.10 (0.02) 0.35 (0.03) 0.09 (0.01) 8.74 (0.31) 1210 3541.2 0.49 motheduc 0.27 0.18 (0.03) 0.11 (0.02) 0.52 (0.03) 0.17 (0.03) 0.11 (0.02) 0.39 (0.03) 0.05 (0.01) 8.74 (0.31) 1230 3785.24 0.45 (0.02) fatheduc abil 0.53 (0.03) www abil_squared Intercept N SSR R-squared 6.94 (0.32) 1230 3999.24 0.35 8.4 (0.12) 1230 3899.97 0.38 Ti + 1) a. Interpret the R-squared of the regression in model 1. b. Can you reject the claim that the returns to ability are linear? Be explicit about the hypothesis and any assumptions you make in order to answer the question. c. Carefully interpret the effect of increasing of L.income by 10. d. How the degrees of freedom in model (3) compare to the degrees of freedom in model (4)? Explain. e. Why did the coefficient of motheduc decrease after adding variable fatheduc to the regression? Discuss and if necessary, incorporate the Gauss-Markov assumptions in your answer.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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1. A researcher is studying the effect of parents' level of education on educational achievement of individuals. Using data on completed years of education (educ), mother's level of education (motheduc), father's level of education (fatheduc), a measure of cognitive ability (abil), and logarithm of family income (I.income), the researcher estimates 4 models. Table (1) shows the OLS estimates for each model, with standard errors in parentheses underneath each coefficient. The dependent variable in all the models is educ. www Model 1 Model 2 Model 3 Model 4 Independent variables Lincome (0.2) 0.15 motheduc 0.27 0.18 (0.03) 0.11 0.17 (0.02) (0.03) 0.11 (0.02) 0.39 (0.03) 0.05 (0.01) 8.74 (0.03) 0.10 (0.02) 0.35 fatheduc (0.02) 0.52 (0.03) abil 0.53 www (0.03) (0.03) 0.09 (0.01) 8.74 (0.31) 1210 3541.2 0.49 abil_squared Intercept 6.94 8.4 N SSR R-squared (0.32) 1230 3999.24 (0.12) 1230 3899,97 (0.31) 1230 3785.24 0.35 0.38 0.45 Ti + 1) a. Interpret the R-squared of the regression in model 1. b. Can you reject the claim that the returns to ability are linear? Be explicit about the hypothesis and any assumptions you make in order to answer the question. c. Carefully interpret the effect of increasing of L.income by 10. d. How the degrees of freedom in model (3) compare to the degrees of freedom in model (4)? Explain. e. Why did the coefficient of motheduc decrease after adding variable fatheduc to the regression? Discuss and if necessary, incorporate the Gauss-Markov assumptions in your answer.
Transcribed Image Text:1. A researcher is studying the effect of parents' level of education on educational achievement of individuals. Using data on completed years of education (educ), mother's level of education (motheduc), father's level of education (fatheduc), a measure of cognitive ability (abil), and logarithm of family income (I.income), the researcher estimates 4 models. Table (1) shows the OLS estimates for each model, with standard errors in parentheses underneath each coefficient. The dependent variable in all the models is educ. www Model 1 Model 2 Model 3 Model 4 Independent variables Lincome (0.2) 0.15 motheduc 0.27 0.18 (0.03) 0.11 0.17 (0.02) (0.03) 0.11 (0.02) 0.39 (0.03) 0.05 (0.01) 8.74 (0.03) 0.10 (0.02) 0.35 fatheduc (0.02) 0.52 (0.03) abil 0.53 www (0.03) (0.03) 0.09 (0.01) 8.74 (0.31) 1210 3541.2 0.49 abil_squared Intercept 6.94 8.4 N SSR R-squared (0.32) 1230 3999.24 (0.12) 1230 3899,97 (0.31) 1230 3785.24 0.35 0.38 0.45 Ti + 1) a. Interpret the R-squared of the regression in model 1. b. Can you reject the claim that the returns to ability are linear? Be explicit about the hypothesis and any assumptions you make in order to answer the question. c. Carefully interpret the effect of increasing of L.income by 10. d. How the degrees of freedom in model (3) compare to the degrees of freedom in model (4)? Explain. e. Why did the coefficient of motheduc decrease after adding variable fatheduc to the regression? Discuss and if necessary, incorporate the Gauss-Markov assumptions in your answer.
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