Regression Statistics               Multiple R 0.92787098               R Square 0.86094455               Adjusted R Square 0.85807743               Standard Error 1.00347554               Observations 100                                 ANOVA                   df SS MS F Significance F       Regression 2 604.745272 302.372636 300.2817255 2.78368E-42       Residual 97 97.6754268 1.006963163           Total 99 702.4206988                                 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 20.0921873 0.139321305 144.2147507 5.5578E-115 19.81567306 20.36870151 19.81567306 20.36870151 Education 0.50217439 0.028267255 17.76523368 2.94696E-32 0.446071712 0.558277064 0.446071712 0.558277064 Experience 0.73253401 0.042662838 17.1703067 3.64357E-31 0.64786009 0.817207937 0.64786009 0.817207937 Discuss the quality of goodness of fit of the model.

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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.