Microsoft Office Tutorials and References

In Depth Information

Exhibit 6.9 shows the dialog box to perform the analysis. The
Input Range
is the

entire table, including labels, and the level of signiﬁcance,

,is0.05

The results of the ANOVA are shown in Table 6.13. The upper section of the

output, entitled
SUMMARY
, shows descriptive statistics for the two factors in the

analysis—Groups (A–E) and Products (a–d). Recall that we will be interested only

in the single factor, Products, and have used the blocks to mitigate the extraneous

effects of skill. The section entitled ANOVA provides the statistics we need to either

accept or reject the null hypothesis: there is no difference in the task completions

times of the 4 software products. All that is necessary for us to reject the hypothesis

is for one of the four software products task completion times to be signiﬁcantly dif-

ferent from the others. Why do we need ANOVA for this determination? Recall we

used the t-Test procedures for comparison of pair-wise differences—two software

products with one compared to another. Of course, there are 6 exhaustive pair-wise

comparisons possible in this problem—a/b, a/c, a/d, b/c, b/d, and c/d. Thus, 6 tests

would be necessary to exhaustively cover all possibilities. It is much easier to use

ANOVA to accomplish the same analysis as the t-Tests, especially as the number of

pairwise comparisons begins to grow large.

α

Table 6.13

ANOVA for analyst example

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