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exists between the target variable y and that particular predictor On the other hand, the F-test considers the linear relationship between the target variable y and the set of predictors (eg, { x1 , x2 , x3 }) taken as a whole The hypotheses for the F-test are given by r H0 : 1 = 2 = = m = 0 r Ha : At least one of the i does not equal 0 The null hypothesis asserts that there is no linear relationship between the target variable y and the set of predictors, x1 , x2 , , xm Thus, the null hypothesis states that the coef cient i for each predictor xi exactly equals zero, leaving the null model to be r Model under H0 : y = 0 + The alternative hypothesis does not assert that the regression coef cients all differ from zero.



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All the rotate options you find in the context menu are new in Acrobat 8. You can rotate images Horizontally and Vertically (180 ), rotate Clockwise and Counterclockwise (90 ), and rotate images using arbitrary rotations. The first four rotation options in the context menu should be self-evident. The more complicated rotation is Rotate Selection that requires a little explanation.





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For the alternative hypothesis to be true, it is suf cient for a single, unspeci ed regression coef cient to differ from zero Hence, the alternative hypothesis for the F-test does not specify a particular model, since it would be true if any, some, or all of the coef cients differed from zero As shown in Table 32, the F-statistic consists of a ratio of two mean squares: the mean square regression (MSR) and the mean square error (MSE) A mean square represents a sum of squares divided by the degrees of freedom associated with that sum of squares statistic Since the sums of squares are always nonnegative, so are the mean squares To understand how the F-test works, we should consider the following.

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has rank M . (a) Show that the linear least-mean-squares estimator of x given both observations {yi, y2}, denoted by 5, satisfies P-'B = PY'Bi.1 P;'&, where P denotes the resulting error 2 covariance matrix and is given by P-' = PC1 P;' - R-' . Remark. This useful result tells us that the estimators {&I, &} and their error covariances { P I ,P2} can be fused together without the need to access the original measurements {v,, g2}.

The MSE is always a good estimate of the overall variance (see model assumption 2) 2 , regardless of whether or not the null hypothesis is true (In fact, recall that we use the standard error of the estimate, s = MSE, as a measure of the usefulness of the regression, without reference to an inferential model) Now, the MSR is also a good estimate of 2 , but only on the condition that the null hypothesis is true If the null hypothesis is false, MSR overestimates 2 So consider the value of F = MSR/MSE with respect to the null hypothesis Suppose that MSR and MSE are close to each other, so that the value of F is small (near 10).

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