Data from which I used excel to do the following three regressions The question is as follows Is the effect of edu on earnings different for men than for women? Specify and estimate a regression that you can use to answer this question. I think the effe
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Given: Data from which I used excel to do the following three regressions
The question is as follows
Is the effect of edu on earnings different for men than for women? Specify and estimate a regression that you can use to answer this question.
I think the effect of educations of more signifcant for men than women but I'm not sure how i would show this is a regression.
![SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R S 0.31182246
Standard Erre 3.06365368
0.56424865
0.31837653
Observations
526
ANOVA
df
MS
Significance F
F
Regression
5
2279.7079 455.941579 48.5769069 3.0428E-41
Residual
520 4880.70641 9.38597387
Total
525 7160.41431
Coefficients Standard Erro.
t Stat
P-value
Lower 95%
Upper 95% Lower 95.0% Upper 95.0%
Intercept
educ
-2.2090053 0.80765943 -2.7350703 0.00644963 -3.7956817 -0.6223289 -3.7956817 -0.6223289
0.60465381 0.05380413 11.2380552 2.2078E-26 0.49895362 0.71035399 0.49895362 0.71035399
0.05894878
0.0112905 5.22109466 2.5761E-07 0.03676818 0.08112939 0.03676818 0.08112939
өxper
female
-2.0832992
0.2722011
-7.6535297 9.5722E-14 -2.6180482 -1.5485502 -2.6180482 -1.5485502
married
0.55718746 0.30554998 1.82355585 0.06879314 -0.0430766 1.15745155
-0.0430766 1.15745155
numdep
0.15806061 0.11274027 1.40198891 0.16151519 -0.0634218 0.37954298
-0.0634218 0.37954298](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fa9b4f571-825b-41b9-ba31-efcae3235105%2F63cf4bf5-e612-4745-bb18-e4a6f9d50988%2Fq91w9m_processed.png&w=3840&q=75)
![SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
0.65682965
0.43142519
Adjusted R S 0.42262712
Standard Err 0.17540702
Observations
526
ANOVA
df
MS
F
Significance F
Regression
8 12.0698639 1.50873299 49.0363833
9.845E-59
Residual
517 15.9068614 0.03076762
Total
525 27.9767253
Coefficients Standard Ero
t Stat
P-value
Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept
0.16187881 0.05486447 2.95052153 0.00331624 0.05409409 0.26966353 0.05409409 0.26966353
educ
0.03301773 0.00387496 8.52079887 1.7436E-16 0.02540513 0.04063033 0.02540513 0.04063033
0.01616746 0.00227787 7.09762926 4.2092E-12 0.01169245 0.02064248 0.01169245 0.02064248
еxper
female
-0.0624946 0.07474726 -0.8360787
0.4034969 -0.2093403 0.08435112 -0.2093403 0.08435112
maried
0.11743652 0.02504955 4.68816907 3.5312E-06
0.0682251 0.16664794
0.0682251 0.16664794
numdep
exper^2
female * edu 0.00134151 0.00572372 0.23437727 0.81478489
female * mar -0.1579155
-0.0144342 0.00677563
-2.1303145 0.03361711
-0.0277454 -0.0011231
-0.0277454 -0.0011231
-0.000299 4.9975E-05
-5.9824669 4.1073E-09
-0.0003972
-0.0002008
-0.0003972 -0.0002008
-0.0099031 0.01258612 -0.0099031 0.01258612
0.0321403
4.9133179 1.2037E-06
-0.2210572 -0.0947739
-0.2210572
-0.0947739
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R S 0.3979933
Standard Erre 0.17910984
Observations
0.63629661
0.40487338
526
ANOVA
df
MS
F
Significance F
Regression
6 11.3270312 1.88783854 58.8472192
1.832E-55
Residual
519 16.6496941 0.03208034
Total
525 27.9767253
Coefficients Standard Ero.
t Stat
P-value
Lower 95%
Upper 95% Lower 95.0% Upper 95.0%
Intercept
0.19491815 0.04723164 4.12685549 4.2837E-05 0.10212945 0.28770685 0.10212945 0.28770685
educ
0.03442776 0.00320564
10.739746 1.9661E-24 0.02813013 0.04072539 0.02813013 0.04072539
exper
0.01626282 0.00232517 6.99423735 8.2538E-12 0.01169491 0.02083073 0.01169491 0.02083073
female
-0.1419812
0.0159141 -8.9217245 7.8791E-18 -0.1732452 -0.1107173 -0.1732452
-0.1107173
married
0.03226401 0.01846498 1.74730847 0.08117529 -0.0040113
0.0685393 -0.0040113
0.0685393
numdep
exper^2
-0.0102891 0.00686473 -1.4988368 0.13452396 -0.0237752 0.00319697
-0.0237752 0.00319697
-0.0002958 5.0991E-05 -5.8018953 1.1414E-08
-0.000396 -0.0001957
-0.000396
-0.0001957](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fa9b4f571-825b-41b9-ba31-efcae3235105%2F63cf4bf5-e612-4745-bb18-e4a6f9d50988%2F9lov7d_processed.png&w=3840&q=75)
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