The total sum of squares is given by the sum of:
A) the squares for the outcome (or dependent variable of your model).
B) squares for the treatment (or independent variable of your model).
C) squared residuals.
D) the squared difference between each observation Y and the average value for Y.

Question

Quizplus · Accepted Answer

The total sum of squares (TSS) is the sum of the squared difference between each observation Y and the average value for Y, and is a measure of the total variation in the data. This is because TSS = SSE + SSR, where SSE is the sum of squared residuals or error variation and SSR is the sum of squared regression or explained variation. So the square of residuals alone (C) is not sufficient to compute TSS, and neither is the square of only the dependent variable (A) or independent variable (B).

The Total Sum of Squares Is Given by the Sum