The given statement is False . That is regression sum of squares (ssr) can be greater than the total sum of squares (sst).
Regression sum of squares (ssr)
The sum of squares regression is the sum of the differences between the predicted value and the mean of the dependent variable.
SSR = ∑( yᵢ-cap - y-bar)² and i = 1 to N
Total Sum of Squares (SST) :
Total Sum of Squares is the deviation of the value of the dependent variable from the sample mean of the dependent variable. Essentially, the total sum of squares quantifies the total sample variation. It can be found using the formula:
SST = ∑(yᵢ - y-bar )²
The first is that the regression sum of squares cannot be computed as a total sum of squares. Let's find an explanation for this. We know that the sum of squares can be divided. There's the dualism of squares, some of the squares and regressions, sums of squares, dead food. A regression sum of squares is not formed, it is a total sum of squares. This means the statement is false. The next processing is the value, which is always positive, but we know that the value the confession of the adjustment, in the range -1 to 1.
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