The regression sum of squares (SSR)can never be greater than the error sum of squares (SSE).

Question

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The regression sum of squares (SSR) measures how much variation in the dependent variable is explained by the independent variables in the regression model. The error sum of squares (SSE) measures how much variation in the dependent variable is not explained by the regression model. It is possible for SSR to be greater than SSE if the regression model is overfitting the data. In other words, if the model is too complex and includes too many independent variables, it may fit the data very well but will not generalize well to new data. This can result in an increase in SSR and a decrease in SSE.

The Regression Sum of Squares (SSR)can Never Be Greater Than