A researcher wants to investigate the relationship between schooling (years of education) and wages (1000 USD). The aim is to find out the "return to education". To be able to investigate this, the researcher collects data among workers and decides to conduct a regression analysis. Additional independent variables are years of working experience ("Experience"), years of working experience-squared ("Experience-sq"), and a dummy variable for the race (if the worker is "Hispanic" then the dummy takes value 1 otherwise 0). She obtains the following regression results. Regression Statistics Multiple R 0.490 R Square A Adjusted R Square 0.234 Standard Error 0.256 Observations 496.000 ANOVA df S MS Significance F Regression 4.000 10.170 2.543 в 0.000 Residual 491.000 32.126 0.065 Total 495.000 42.296 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -0.632 0.110 -5.751 0.000 -0.847 -0.416 Schooling 0.065 0.006 0.000 D E Race dummy -0.073 0.042 -1.741 0.082 -0.155 0.009 Experience 0.063 0.012 5.463 0.000 0.041 0.086 Experience-sq -0.002 0.001 -2.829 0.005 -0.003 -0.001

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Chapter1: Starting With Matlab
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What is C,D and E? And how do i calculate it??

A researcher wants to investigate the relationship between schooling (years of education) and
wages (1000 USD). The aim is to find out the "return to education". To be able to investigate this,
the researcher collects data among workers and decides to conduct a regression analysis.
Additional independent variables are years of working experience ("Experience"), years of working
experience-squared ("Experience-sq"), and a dummy variable for the race (if the worker is
"Hispanic" then the dummy takes value 1 otherwise 0). She obtains the following regression results.
Regression Statistics
Multiple R
0.490
R Square
A
Adjusted R Square
0.234
Standard Error
0.256
Observations
496.000
ANOVA
df
S
MS
F
Significance F
Regression
4.000
10.170
2.543
в
0.000
Residual
491.000
32.126
0.065
Total
495.000
42.296
Coefficients Standard Error
t Stat
P-value
Lower 95% Upper 95%
Intercept
-0.632
0.110
-5.751
0.000
-0.847
-0.416
Schooling
0.065
0.006
0.000
D
Race dummy
-0.073
0.042
-1.741
0.082
-0.155
0.009
Experience
0.063
0.012
5.463
0.000
0.041
0.086
Experience-sq
-0.002
0.001
-2.829
0.005
-0.003
-0.001
Transcribed Image Text:A researcher wants to investigate the relationship between schooling (years of education) and wages (1000 USD). The aim is to find out the "return to education". To be able to investigate this, the researcher collects data among workers and decides to conduct a regression analysis. Additional independent variables are years of working experience ("Experience"), years of working experience-squared ("Experience-sq"), and a dummy variable for the race (if the worker is "Hispanic" then the dummy takes value 1 otherwise 0). She obtains the following regression results. Regression Statistics Multiple R 0.490 R Square A Adjusted R Square 0.234 Standard Error 0.256 Observations 496.000 ANOVA df S MS F Significance F Regression 4.000 10.170 2.543 в 0.000 Residual 491.000 32.126 0.065 Total 495.000 42.296 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -0.632 0.110 -5.751 0.000 -0.847 -0.416 Schooling 0.065 0.006 0.000 D Race dummy -0.073 0.042 -1.741 0.082 -0.155 0.009 Experience 0.063 0.012 5.463 0.000 0.041 0.086 Experience-sq -0.002 0.001 -2.829 0.005 -0.003 -0.001
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