The variable descriptions and Stata outputs from the simple and multiple linear regression are available in the picture that i have provided. Using those informations, can you answer the following questions. - Firstly, report the results from the regression of wage on educ in the form of a fitted line, with the standard error of coefficients presented in parentheses underneath the corresponding coefficients. Round the numbers to two decimal places. - Is the coefficient of educ statistically significant? - Now consider the multiple linear regression that includes KWW as one

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  1. The variable descriptions and Stata outputs from the simple and multiple linear regression are available in the picture that i have provided. Using those informations, can you answer the following questions.

- Firstly, report the results from the regression of wage on educ in the form of a fitted line, with the standard error of coefficients presented in parentheses underneath the corresponding coefficients. Round the numbers to two decimal places.

- Is the coefficient of educ statistically significant?

- Now consider the multiple linear regression that includes KWW as one of the explanatory variables. Between this regression and the simple linear regression in part (a), which model is more likely to measure the ceteris paribus effect of education on wages? Explain and when possible use evidence to support your answer. 

Can you also provide the resource as well, please?

reg wage educ
Source
Model
Residual
Total
wage
educ
_cons
Variable name
wage
educ
KWW
SS
16340644.5
136375524
Figure (1)
152716168
Variable description
monthly earnings
years of education
General test of work-
related skills
Figure (2)
df
16340644.5
1
933 146168.836
MS
934 163507.675
Coefficient std. err.
60.21428 5.694982
146.9524
77.71496
Number of obs
F(1, 933)
Prob > F
R-squared
Adj R-squared
Root MSE
t P>|t|
10.57
0.000
1.89 0.059
=
49.03783
-5.56393
=
=
935
111.79
0.0000
0.1070
0.1060
382.32
[95% conf. interval]
71.39074
299.4688
Transcribed Image Text:reg wage educ Source Model Residual Total wage educ _cons Variable name wage educ KWW SS 16340644.5 136375524 Figure (1) 152716168 Variable description monthly earnings years of education General test of work- related skills Figure (2) df 16340644.5 1 933 146168.836 MS 934 163507.675 Coefficient std. err. 60.21428 5.694982 146.9524 77.71496 Number of obs F(1, 933) Prob > F R-squared Adj R-squared Root MSE t P>|t| 10.57 0.000 1.89 0.059 = 49.03783 -5.56393 = = 935 111.79 0.0000 0.1070 0.1060 382.32 [95% conf. interval] 71.39074 299.4688
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What happened to the standard error of educ after adding KWW to the model? Discuss. 

Do you agree or disagree with the following statement? “If the log of the dependent variable appears in the regression, changing the unit of measurement of any independent variable affects both the slope and intercept coefficients”. Discuss by providing the resource.

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