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146      4 Parametric Tests of Hypotheses


              In the configuration of  Figure  4.13b  (null hypothesis is  false), the  between-
           group variance no longer represents an estimate of the population variance. In this
           case, we obtain a ratio v B/v W larger than 1. (In this case the contribution of v B to the
           final value of v in 4.30 is smaller than the contribution of v W.)
              The  one-way ANOVA, assuming the test conditions are satisfied, uses the
           following test statistic (see properties of the F distribution in section B.2.9):

                   v    MSB
                *
              F =   B  =      ~   F c− , n− c   (under H 0).               4.33
                                    1
                   v W  MSE

                                *
              If H 0 is not true, then F  exceeds 1 in a statistically significant way.
              The F distribution can be used even when there are mild deviations from the
           assumptions of normality and equality of variances. The equality of variances can
           be assessed  using the  ANOVA generalization  of  Levene’s test described in the
           section 4.4.2.2.

           Table 4.9. Critical F values at α = 0.05 for n = 25 and several values of c.
             c             2       3        4       5       6       7        8

             F c−1,n−c   4.26    3.42    3.05    2.84     2.71    2.63    2.58


              For c = 2, it can be proved that the ANOVA test is identical to the t test for two
           independent samples. As c increases, the 1 – α percentile of F c−1,n−c decreases (see
           Table 4.9), rendering the rejection of the null hypothesis “easier”. Equivalently, for
                                                                     *
           a certain level of confidence the  probability of  observing a given  F  under H 0
           decreases. In  section  4.5.1,  we have already made use of the fact that the null
                                                   c
                       c
           hypothesis for   > 2 is more “restrictive” than for   = 2.
              The previous sums of squares can be shown to be computable as follows:

                    c  r i
                              2
                                n
              SST  = ∑∑ x ij 2  −T / ,                                    4.34a
                    = i 1  = j 1
                    c
                        2
                                2
              SSB = ∑  ( T /  r )  −T / ,                                 4.34b
                                  n
                           i
                        i
                    = i 1

           where T i and T are the totals along the columns and the grand total, respectively.
           These last formulas are useful for manual computation (or when using EXCEL).
           Example 4.13
           Q: Consider the  variable ART of the  Cork Stoppers’ dataset. Is there
           evidence,  provided by the  samples, that  the three classes correspond to three
           different populations?
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