12-2 HYPOTHESIS TESTS IN MULTIPLE LINEAR REGRESSION 435EXAMPLE 12-5 Consider the wire bond pull strength data in Example 12-1. We will investigate the contribution of
the variable x 2 (die height) to the model using the partial F-test approach. That is, we wish to testTo test this hypothesis, we need the extra sum of squares due to 2 , orIn Example 12-3 we have calculatedand from Example 11-8, where we fit the model Y 0 1 x 1 , we can calculateTherefore,This is the increase in the regression sum of squares due to adding x 2 to a model already con-
taining x 1. To test H 0 : 2 0, calculate the test statisticNote that the MSEfrom the full model, using both x 1 and x 2 , is used in the denominator of the
test statistic. Since f0.05,1,224.30, we reject H 0 : 2 0 and conclude that the regressor die
height (x 2 ) contributes significantly to the model.
Table 12-4 shows the Minitab regression output for the wire bond pull strength data. Just
below the analysis of variance summary in this table the quantity labeled “Seq SS” shows the
sum of squares obtained by fitting x 1 alone (5885.9) and the sum of squares obtained by fitting
x 2 after x 1. Notationally, these are referred to above as and.Since the partial F-test in the above example involves a single variable, it is equivalent to
the t-test. To see this, recall from Example 12-5 that the t-test on H 0 : 2 0 resulted in the test
statistic t 0 4.4767. Furthermore, the square of a t-random variable with degrees of free-
dom is an F-random variable with one and degrees of freedom, and we note that
(4.4767)^2 20.04f 0.12-2.3 More About the Extra Sum of Squares Method (CD Only)t^20SSR 1 1 0 02 SSR 1 2 0 1 , 02f 0 SSR 1 2 0 1 , 02 1
MSE
104.9191 1
5.2352
20.04104.9191 1 one degree of freedom 2
SSR 1 2 0 1 , 02 5990.77125885.85215885.8521 1 one degree of freedom 2
SSR 1 1 0 02 ˆ 1 Sxy 1 2.9027 21 2027.7132 2SSR 1 1 , 2 0 02 ˆ¿X¿yaani 1yibn 5990.7712^1 two degrees of freedom^2
SSR 1 1 , 2 0 02 SSR 1 1 0 02SSR 1 2 0 1 , 02 SSR 1 1 , 2 , 02 SSR 1 1 , 02H 1 : 2
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