The R squared value, called the coefficient of determination, determines how well the data points fit on a regression equation. More specifically, the R squared value is a measure of how the independent variables in a regression equation explain the variables of the dependent variable. The value of R squared can change based on the inclusion or removal of variables in the regression model. R squared values are typically used as a measure of the effectiveness of a model.  Hence, a high R squared value (anything above 55%), can be an indicator of a capable model. Continue reading The implication of R-Squared →