The following results were obtained by regressing mean hourly wage in dollars (Y) on years of schooling (X). Dependent Variable: MEAN_WAGE Method: Least Squares Date: 02/15/15 Time: 11:11 Sample: 1 13 Included observations: 13 VariableCoefficientStd. Errort-StatisticProb. C-0.0144530.874624-0.0165250.9871 YEARS_SCHOOLING0.7240970.06958110.406480.0000 R-squared0.907791 Mean dependent var8.674708 Adjusted R-squared0.899409 S.D. dependent var2.959706 S.E. of regression0.938704 Akaike info criterion2.852004 Sum squared resid9.692810 Schwarz criterion2.938920 Log likelihood-16.53803 Hannan-Quinn criter.2.834139 F-statistic108.2948 Durbin-Watson stat1.737984 Prob(F-statistic)0.000000 Interpret the regression coefficients. Do they make economic sense?Would you conclude that there is a statistically significant relationship at the 5% level between mean hourly wage (Y) and years of schooling (X)? (Hint: Test H0: β2=0)Comment about the goodness of fit of the above regression model?Test the null hypothesis, H0:β2=1, (vs. H1: β2≠1) at the 1% level of significance. 1. The following results were obtained by regressing hourly wage on years of schooling. (6marks)Dependent Variable: Hourly wageMethod: Least SquaresIncluded observations: 13VariableCoefficient Std. Error t-StatisticProb.InterceptYears of Schooling 0.724097-0.0144530.874624 -0.0165250.98710.06958110.406480.0000R-squaredAdjusted R-squared 0.899409 S.D. dependent varS.E. of regressionSum squared residLog likelihoodF-statistic0.907791 Mean dependent var 8.6747082.9597060.938704 Akaike info criterion 2.8520049.692810 Schwarz criterion2.938920-16.53803 Hannan-Quinn criter. 2.834139108.2948 Durbin-Watson stat1.737984Prob(F-statistic) 0.000000a. Interpret the regression coefficients. Do they make economic sense?b. Analyze whether the intercept and slope coefficient are statistically significant at the 5%level.c. Comment about the goodness of fit of the above regression model using r-squared andadjusted r-squared? Also differentiate between r-squared and adjusted r-squared.d. Test the null hypothesis, Ho:B2=0.75, (vs. H1: B2#0.75) at the 5% level of significance.e. Explain whether the overall model is statistically significant at the 5% level.

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Description: The following results were obtained by regressing mean hourly wage in dollars (Y) on years of schooling (X). Dependent Variable: MEAN_WAGE Method: Least Squares Date: 02/15/15 Time: 11:11 Sample: 1 13 Included observations: 13 Var

In simple linear regression, there is only one dependant variable and one independent variable. The…

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The following results were obtained by regressing mean hourly wage in dollars (Y) on years of schooling (X).

Dependent Variable: MEAN_WAGE
Method: Least Squares
Date: 02/15/15 Time: 11:11
Sample: 1 13
Included observations: 13


Variable	Coefficient	Std. Error	t-Statistic	Prob.


C	-0.014453	0.874624	-0.016525	0.9871
YEARS_SCHOOLING	0.724097	0.069581	10.40648	0.0000


R-squared	0.907791	Mean dependent var	8.674708
Adjusted R-squared	0.899409	S.D. dependent var	2.959706
S.E. of regression	0.938704	Akaike info criterion	2.852004
Sum squared resid	9.692810	Schwarz criterion	2.938920
Log likelihood	-16.53803	Hannan-Quinn criter.	2.834139
F-statistic	108.2948	Durbin-Watson stat	1.737984
Prob(F-statistic)	0.000000

Interpret the regression coefficients. Do they make economic sense?
Would you conclude that there is a statistically significant relationship at the 5% level between mean hourly wage (Y) and years of schooling (X)? (Hint: Test H₀: β₂=0)
Comment about the goodness of fit of the above regression model?
Test the null hypothesis, H₀:β₂=1, (vs. H₁: β₂≠1) at the 1% level of significance.

1. The following results were obtained by regressing hourly wage on years of schooling. (6
marks)
Dependent Variable: Hourly wage
Method: Least Squares
Included observations: 13
Variable
Coefficient Std. Error t-Statistic
Prob.
Intercept
Years of Schooling 0.724097
-0.014453
0.874624 -0.016525
0.9871
0.069581
10.40648
0.0000
R-squared
Adjusted R-squared 0.899409 S.D. dependent var
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
0.907791 Mean dependent var 8.674708
2.959706
0.938704 Akaike info criterion 2.852004
9.692810 Schwarz criterion
2.938920
-16.53803 Hannan-Quinn criter. 2.834139
108.2948 Durbin-Watson stat
1.737984
Prob(F-statistic) 0.000000
a. Interpret the regression coefficients. Do they make economic sense?
b. Analyze whether the intercept and slope coefficient are statistically significant at the 5%
level.
c. Comment about the goodness of fit of the above regression model using r-squared and
adjusted r-squared? Also differentiate between r-squared and adjusted r-squared.
d. Test the null hypothesis, Ho:B2=0.75, (vs. H1: B2#0.75) at the 5% level of significance.
e. Explain whether the overall model is statistically significant at the 5% level.

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