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Expert-verified Found in: Page 192 ### Elementary Statistics

Book edition 9th
Author(s) Weiss, Neil
Pages 590 pages
ISBN 9780321989505 # For each of the sums of squares in regression, state its name and what it measures,a. SSTb. SSRc. SSE

a) SST measures how much variation is there in the observed data.

b) SSR measures the variation in the modeling errors.

c) SSE (Sum of squares Error) is the difference between the observed value and the predicted value.

See the step by step solution

## Part(a) Step 1: Given Information

To determine the name of SST and to explain what it measures.

## Part (a) Step 2: Explanation

The regression sum of squares is obtained by dividing the corresponding sum of squares by the degrees of freedom. In regression, whether the terms in the model are significant or not is determined by using the mean squares. The mean square is the term obtained by dividing the sum of squares by the degrees of freedom.

SST (Sum of Squares Total) is the squared difference between the observed dependent variable and its mean.

## Part (b) Step 1: Given Information

To determine the name of SSR and to explain what it measures.

## Part (b) Step 2: Explanation

S S R (Sum of Squares due to Regression) is the sum of the differences between the predicted value and the mean of the dependent variable.

## Part (c) Step 1: Given Information

To determine the name of S S E and to explain what it measures.

## Part (c) Step 2: Explanation

SSE measures how much variation is there in the modeled values and this is compared to the total sum of squares and to the residual sum of squares. ### Want to see more solutions like these? 