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Consider the following OLS regression output which is estimated with Wage2.dta (see tutorial folder on Blackboard) 935 36.82 0.0000 0.1367 0.1330 .39214 Source df MS Number of obs F(4, 930) 4 5.66168416 930 םר15377. Model 22.6467366 143.009547 Prob > r Residual R-squared Adj R-squared Total 165.656283 934 .177362188 Root MSE 1wage Coef. Std. Err. (951 Conf. Interval] .0400982 .005914 .0035899 -.0000842 5.649044 0.000 0.000 0.783 0.517 0.000 .0266843 .0039579 -.0219954 -.0003389 5.013117 edue .0068351 5.87 .0535122 .0009967 .013037 .0001298 .3240363 5.93 0.28 -0.65 17.43 .0078701 .0291752 .0001706 6.284971 10 hours hours2 cons where Iwage is the natural logarithm of monthly wage in US$, educ is years of education, IQ is points on an IQ intelligence test, hours is average weekly hours worked, and hours2 is experience squared (hours hours). Assume that MLR 1-6 hold. 10) At how many average weekly hours worked is the marginal effect of hours on predicted wage equal to zero? 11) What is the exact effect (in percent) of an increase of 10 years of education on predicted wage?

Consider the following OLS regression output which is estimated with Wage2.dta (see tutorial folder on Blackboard) 935 36.82 0.0000 0.1367 0.1330 .39214 Source df MS Number of obs F(4, 930) 4 5.66168416 930 םר15377. Model 22.6467366 143.009547 Prob > r Residual R-squared Adj R-squared Total 165.656283 934 .177362188 Root MSE 1wage Coef. Std. Err. (951 Conf. Interval] .0400982 .005914 .0035899 -.0000842 5.649044 0.000 0.000 0.783 0.517 0.000 .0266843 .0039579 -.0219954 -.0003389 5.013117 edue .0068351 5.87 .0535122 .0009967 .013037 .0001298 .3240363 5.93 0.28 -0.65 17.43 .0078701 .0291752 .0001706 6.284971 10 hours hours2 cons where Iwage is the natural logarithm of monthly wage in US$, educ is years of education, IQ is points on an IQ intelligence test, hours is average weekly hours worked, and hours2 is experience squared (hours hours). Assume that MLR 1-6 hold. 10) At how many average weekly hours worked is the marginal effect of hours on predicted wage equal to zero? 11) What is the exact effect (in percent) of an increase of 10 years of education on predicted wage?

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 2AGP
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Hi Just question 10 and 11 

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Consider the following OLS regression output which is estimated with Wage2.dta (see tutorial folder on Blackboard) Source df MS Number of obs 935 F(4, 930) 36.82 Model 22.6467366 4 5.66168416 Prob > F 0.0000 Residual 143.009547 930 6ור1537- R-squared Adj R-squared 0.1367 0.1330 Total 165.656283 934 .177362188 Root MSE .39214 lwage Coef. Std. Err. [95% Conf. Interval) t .0068351 .0535122 .0078701 educ .0400982 5.87 0.000 .0266843 1Q .005914 .0009967 5.93 0.000 .0039579 hours .0035899 .013037 0.28 0.783 0.517 -.0219954 -.0003389 .0291752 .0001706 hours2 -.0000842 .0001298 -0.65 cons 5.649044 .3240363 17.43 0.000 5.013117 6.284971 where Iwage is the natural logarithm of monthly wage in USS, educ is years of education, IQ is points on an IQ intelligence test, hours is average weekly hours worked, and hours2 is experience squared (hours * hours). Assume that MLR 1-6 hold. 10) At how many average weekly hours worked is the marginal effect of hours on predicted wage equal to zero? 11) What is the exact effect (in percent) of an increase of 10 years of education on predicted wage?
Transcribed Image Text:Consider the following OLS regression output which is estimated with Wage2.dta (see tutorial folder on Blackboard) Source df MS Number of obs 935 F(4, 930) 36.82 Model 22.6467366 4 5.66168416 Prob > F 0.0000 Residual 143.009547 930 6ור1537- R-squared Adj R-squared 0.1367 0.1330 Total 165.656283 934 .177362188 Root MSE .39214 lwage Coef. Std. Err. [95% Conf. Interval) t .0068351 .0535122 .0078701 educ .0400982 5.87 0.000 .0266843 1Q .005914 .0009967 5.93 0.000 .0039579 hours .0035899 .013037 0.28 0.783 0.517 -.0219954 -.0003389 .0291752 .0001706 hours2 -.0000842 .0001298 -0.65 cons 5.649044 .3240363 17.43 0.000 5.013117 6.284971 where Iwage is the natural logarithm of monthly wage in USS, educ is years of education, IQ is points on an IQ intelligence test, hours is average weekly hours worked, and hours2 is experience squared (hours * hours). Assume that MLR 1-6 hold. 10) At how many average weekly hours worked is the marginal effect of hours on predicted wage equal to zero? 11) What is the exact effect (in percent) of an increase of 10 years of education on predicted wage?
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