(1) Earnings on Height (2) Earnings on Height , LT HS, HS, Some Col as control variables Use the data Earnings and Height the file Earnings and Height Description pdf contains a description of the variables Unfortunately, there isnt a direct measure of cognitive ability in the data set, but the data set does include years of education for each individual Because students with higher cognitive ability are more likely to attend school longer, years of education might serve as a control variable for cognitive ability in this case, including education in the regression will eliminate, or at least attenuate, the omitted variable bias problem Use the years of education variable (educ) to construct four indicator variables for whether a worker has less than a high school diploma (LT HS 1 if educ Focusing on women only, run a regression of (1) Earnings on Height and (2) Earnings on Height, LT HS, HS, and Some Col as control variables Compare the estimated coefficient on Height in regressions (1) and (2) Is there a large change in the coefficient Hs it changed in a way consistent with the cognitive ability explanation Explain The regression omits the control variable College Why Test the joint null hypothesis that the coefficients on the education variables are equal to 0 Discuss the values of the estimated coefficients on LT HS, HS, and Some Col What do the coefficients measure Hello I need some help with question 1 and 2 Thanks Call Im(formula earnings height, data femaledata) Residuals Min 1Q Median 42748 22006 7466 3Q 36641 Max 46865 Coefficients Estimate Std Error t value Pr( ltl) (Intercept) 12650 9 6383 7 1 982 0 0475 height 511 2 98 9 5 169 2 4e 07 Signif codes 0 ' 0 001 ' 0 01 ' 0 05 ' ' 0 1 ' 1 Residual standard error 26800 on 9972 degrees of freedom Multiple R squared 0 002672, Adjusted R squared 0 002572 F statistic 26 72 on 1 and 9972 DF, p value 2 396e 07 Call Im(formula earnings height, data femaledata) Residuals Min 1Q Median 47836 21879 7976 3Q 34323 Max 50599 Coefficients Estimate Std Error t value Pr( ltl) (Intercept) 512 73 3386 86 0 151 0 88 height 707 67 50 49 14 016 ltl) (Intercept) 12650 9 6383 7 1 982 0 0475 height 511 2 98 9 5 169 2 4e 07 Signif codes 0 ' 0 001 ' 0 01 ' 0 05 ' ' 0 1 ' 1 Residual standard error 26800 on 9972 degrees of freedom Multiple R squared 0 002672, Adjusted R squared 0 002572 F statistic 26 72 on 1 and 9972 DF, p value 2 396e 07 Call Im(formula earnings height, data femaledata) Residuals Min 1Q Median 47836 21879 7976 3Q 34323 Max 50599 Coefficients Estimate Std Error t value Pr( ltl) (Intercept) 512 73 3386 86 0 151 0 88 height 707 67 50 49 14 016

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(1) Earnings on Height (2) Earnings on Height , LT_HS, HS, Some_Col as control variables Use the data Earnings_and Height; the file Earnings_and_Height_Description.pdf contains a

(1) Earnings on Height

(2) Earnings on Height, LT_HS, HS, Some_Col as control variables

image text in transcribed Use the data Earnings_and Height; the file Earnings_and_Height_Description.pdf contains a description of the variables. Unfortunately, there isnt a direct measure of cognitive ability in the data set, but the data set does include years of education for each individual. Because students with higher cognitive ability are more likely to attend school longer, years of education might serve as a control variable for cognitive ability; in this case, including education in the regression will eliminate, or at least attenuate, the omitted variable bias problem.

Use the years of education variable (educ) to construct four indicator variables for whether a worker has less than a high school diploma (LT_HS = 1 if educ

Focusing on women only, run a regression of: (1) Earnings on Height and (2) Earnings on Height, LT_HS, HS, and Some_Col as control variables.

Compare the estimated coefficient on Height in regressions (1) and (2). Is there a large change in the coefficient? Hs it changed in a way consistent with the cognitive ability explanation? Explain.
The regression omits the control variable College. Why?
Test the joint null hypothesis that the coefficients on the education variables are equal to 0.
Discuss the values of the estimated coefficients on LT_HS, HS, and Some_Col. What do the coefficients measure?

Hello! I need some help with question 1 and 2. Thanks!

Call: Im(formula earnings ~ height, data = femaledata) Residuals: Min 1Q Median -42748 -22006 -7466 3Q 36641 Max 46865 Coefficients: Estimate Std. Error t value Pr(>ltl) (Intercept) 12650.9 6383.7 1.982 0.0475 * height 511.2 98.9 5.169 2.4e-07 *** Signif. codes: 0 -***' 0.001 "**' 0.01 "*' 0.05 '.' 0.1 ' 1 Residual standard error: 26800 on 9972 degrees of freedom Multiple R-squared: 0.002672, Adjusted R-squared: 0.002572 F-statistic: 26.72 on 1 and 9972 DF, p-value: 2.396e-07 Call: Im(formula earnings ~ height, data femaledata) Residuals: Min 1Q Median -47836 -21879 -7976 3Q 34323 Max 50599 Coefficients: Estimate Std. Error t value Pr(>ltl) (Intercept) -512.73 3386.86 -0.151 0.88 height 707.67 50.49 14.016 ltl) (Intercept) 12650.9 6383.7 1.982 0.0475 * height 511.2 98.9 5.169 2.4e-07 *** Signif. codes: 0 -***' 0.001 "**' 0.01 "*' 0.05 '.' 0.1 ' 1 Residual standard error: 26800 on 9972 degrees of freedom Multiple R-squared: 0.002672, Adjusted R-squared: 0.002572 F-statistic: 26.72 on 1 and 9972 DF, p-value: 2.396e-07 Call: Im(formula earnings ~ height, data femaledata) Residuals: Min 1Q Median -47836 -21879 -7976 3Q 34323 Max 50599 Coefficients: Estimate Std. Error t value Pr(>ltl) (Intercept) -512.73 3386.86 -0.151 0.88 height 707.67 50.49 14.016