SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R S 0.3979933 0.63629661 0.40487338 Standard Erre 0.17910984 Observations 526 ANOVA df MS F Significance F Regression Residual 6 11.3270312 1.88783854 58.8472192 519 16.6496941 0.03208034 1.832E-55 Total 525 27.9767253 Coefficients Standard Ero. 0.19491815 0.04723164 4.12685549 4.2837E-05 0.10212945 0.28770685 0.10212945 0.28770685 Upper 95% Lower 95.0% Upper 95.0% t Stat P-value Lower 95% Intercept educ 0.03442776 0.00320564 10.739746 1.9661E-24 0.02813013 0.04072539 0.02813013 0.04072539 0.01626282 0.00232517 6.99423735 8.2538E-12 0.01169491 0.02083073 0.01169491 0.02083073 өxper 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
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.
Run a regression of the logarithm of average hourly earnings, ln(wage) on educ, exper, exper2, female, married and numdep. Discuss the statistical significance of each of the slope coefficients and interpret the coefficient on female and married.
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