The change in the dependent value for a variation in the independent value is estimated in the regression analysis. Multiple regression takes into consideration all the assumptions of correlation. It takes place when the independent variable is dichotomy. In the above prescribed case, if the increase of men and women were to be considered separately; Multiple regression is used. In the case of linear correlation no power terms are found as it will not reflect curvilinear changes in independent variables. In the context of multiple regression the powers to the variables were found to represent the curvilinear variations in independent and dependent variables. Correlation is the percent of variance in the dependent explained by the given independent when all other independents are allowed to vary. In the final result the magnitude of r2 reflects not only the unique covariance it shares with the dependent, but uncontrolled effects on the dependent attributable to covariance the given independent shares with other independents in the model. For example in the above case the increase of male female population can be taken as covariance.
2. During the years 1790 to 1820, the correlation between the number of churches built in New England and the barrels of Rum imported into the region was a perfect 1.0.