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A method called the analysis of variance can be used to test for signi cance of regression. The procedure partitions the total variability in the response variable into meaningful components as the basis for the test. The analysis of variance identity is as follows:

y 22

(11-24)

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The two components on the right-hand-side of Equation 11-24 measure, respectively, the amount of variability in yi accounted for by the regression line and the residual variation left n unexplained by the regression line. We usually call SSE g i 1 1yi y i 2 2 the error sum of n 2 squares and SSR g i 1 1 yi y 2 the regression sum of squares. Symbolically, Equation 11-24 may be written as

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(11-25)

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where SST g i 1 1 yi y2 2 is the total corrected sum of squares of y. In Section 11-2 we noted that SSE SST 1Sxy (see Equation 11-14), so since SST 1Sxy SSE, we note that the regression sum of squares in Equation 10-26 is SSR 1Sxy. The total sum of squares SST has n 1 degrees of freedom, and SSR and SSE have 1 and n 2 degrees of freedom, respectively. 2 2 2 2 , E1SSR 2 We may show that E3SSE 1n 22 4 and 1Sx x and that SSE 2 SSR are independent chi-square random variables with n 2 and 1 degrees of freedom, respectively. Thus, if the null hypothesis H0: 1 0 is true, the statistic

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(11-26)

follows the F1,n 2 distribution, and we would reject H0 if f0 f ,1,n 2. The quantities MSR SSR 1 and MSE SSE (n 2) are called mean squares. In general, a mean square is always computed by dividing a sum of squares by its number of degrees of freedom. The test procedure is usually arranged in an analysis of variance table, such as Table 11-3.

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Table 11-3 Analysis of Variance for Testing Signi cance of Regression Source of Variation Regression Error Total

EXAMPLE 11-3

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We will use the analysis of variance approach to test for signi cance of regression using the oxygen purity data model from Example 11-1. Recall that SST 173.38, 1 14.947, Sxy 10.17744, and n 20. The regression sum of squares is SSR and the error sum of squares is SSE SST SSR 173.38 152.13 21.25

114.947210.17744