Question

If the regression analysis reported that the total sum of squares (SST) is 33.67 and the residual sum of squares (SSE) is 18.92, then what is the regression sum of squares (SSR)? Explain SST, SSE, and SSR, respectively

Answer 1

SST = SSR + SSE

Thus, SSR = SST - SSE = 33.67 - 18.92 = 14.75

SST ( total sum of squares) is the sum of square of deviations between the original values and mean of the values.

SSR ( regression sum of squares) is the sum of squared deviations between predicted values (values or observations that are predicted using regression technique) and the overall mean of original values. This is also called as explained sum of squares because it indicates the variability that is explained by the model.

SSE (residual or error sum of squares) is the sum of square of differences between the original values and the predicted values. This difference gives us the error in our prediction.