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Q. 14

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Elementary Statistics

Found in: Page 192

Book edition 9th

Author(s) Weiss, Neil

Pages 590 pages

ISBN 9780321989505

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Short Answer

For each of the sums of squares in regression, state its name and what it measures,
a. SST
b. SSR
c. 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.

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Step by Step Solution

TABLE OF CONTENTS :

TABLE OF CONTENTS

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.

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