Final answer:
The sum of squares of regression (SSR) can be calculated by subtracting the sum of squared errors (SSE) from the total sum of squares (SST).
Step-by-step explanation:
The sum of squares of regression (SSR) can be calculated by subtracting the sum of squared errors (SSE) from the total sum of squares (SST). In other words, SSR = SST - SSE.
For example, if the SST is 2440 and the SSE is 200, then SSR = 2440 - 200 = 2240.
The SSR represents the variation explained by the regression model, while the SSE represents the unexplained variation or residual error in the model.