Given: Regression in picture Using data I ran a regression of average hourly earnings (wage) on education (educ), experience (exper), exper2, sex (female), marital status (married) and num- ber of dependents (numdep). The question is as follows: Discuss the statistical significance of each of the slope coefficients and interpret the coefficients on female and married. If experience increases from 20 years to 21 years, how are ea

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Given: Regression in picture

Using data I ran a regression of average hourly earnings (wage) on education (educ), experience (exper), exper2, sex (female), marital status (married) and num- ber of dependents (numdep).

The question is as follows:

Discuss the statistical significance of each of the slope coefficients and interpret the coefficients on female and married. If experience increases from 20 years to 21 years, how are earnings expected to change? If experience increases from 35 years to 36 years, how are earnings expected to change? Compare the results.

SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R S 0.34294939
Standard Ere 2.99356599
0.59199539
0.35045854
Observations
526
ANOVA
Significance F
6 2509.42832 418.238054 46.6708673 9.9813E-46
df
MS
Regression
Residual
519 4650.98599 8.96143736
Total
525 7160.41431
Coefficients Standard Erro.
t Stat
P-value
Lower 95%
Upper 95% Lower 95.0% Upper 95.0%
Intercept
-2.3048758 0.78940961 -2.9197463 0.00365539 -3.8557068
-0.7540449 -3.8557068 -0.7540449
educ
0.5523733 0.05357771 10.3097596 8.4633E-23 0.44711746 0.65762914 0.44711746 0.65762914
0.24761349 0.03886197 6.37161435 4.1299E-10 0.17126738 0.32395959 0.17126738 0.32395959
exper
female
-2.0934987 0.26598154 -7.8708418 2.0671E-14 -2.6160315 -1.5709659 -2.6160315 -1.5709659
married
0.16156641
0.3086158 0.52351955 0.60083621 -0.4447233 0.76785614
-0.4447233 0.76785614
numdep
-0.0043109 0.11473426
-0.037573 0.97004262 -0.2297116 0.22108975
-0.2297116 0.22108975
exper^2
-0.0043149 0.00085224 -5.0630355 5.7409E-07 -0.0059892 -0.0026407
-0.0059892
-0.0026407
Transcribed Image Text:SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R S 0.34294939 Standard Ere 2.99356599 0.59199539 0.35045854 Observations 526 ANOVA Significance F 6 2509.42832 418.238054 46.6708673 9.9813E-46 df MS Regression Residual 519 4650.98599 8.96143736 Total 525 7160.41431 Coefficients Standard Erro. t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -2.3048758 0.78940961 -2.9197463 0.00365539 -3.8557068 -0.7540449 -3.8557068 -0.7540449 educ 0.5523733 0.05357771 10.3097596 8.4633E-23 0.44711746 0.65762914 0.44711746 0.65762914 0.24761349 0.03886197 6.37161435 4.1299E-10 0.17126738 0.32395959 0.17126738 0.32395959 exper female -2.0934987 0.26598154 -7.8708418 2.0671E-14 -2.6160315 -1.5709659 -2.6160315 -1.5709659 married 0.16156641 0.3086158 0.52351955 0.60083621 -0.4447233 0.76785614 -0.4447233 0.76785614 numdep -0.0043109 0.11473426 -0.037573 0.97004262 -0.2297116 0.22108975 -0.2297116 0.22108975 exper^2 -0.0043149 0.00085224 -5.0630355 5.7409E-07 -0.0059892 -0.0026407 -0.0059892 -0.0026407
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