The value of the sum of squares for regression, SSR, can never be smaller than 0.0.Group of answer choicesTrueFalse

Question

The value of the sum of squares for regression, SSR, can never be smaller than 0.0.

Group of answer choices

True
False

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Solution

Answer

The statement is True.

Explanation

The sum of squares for regression (SSR) is a measure of the variation explained by the regression model. It represents the difference between the total sum of squares (SST) and the sum of squares for error (SSE). Since both SST and SSE are based on squared values, they are always non-negative. Therefore, SSR, which is computed as:

$SSR = SST - SSE$

can never be less than 0. If the model explains no variation (SSE equals SST), then SSR will equal 0. Otherwise, SSR will be a positive value. Hence, the sum of squares for regression (SSR) is always greater than or equal to 0.

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The value of the sum of squares for regression, SSR, can never be smaller than 0.0.Group of answer choicesTrueFalse

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