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Additional info for Business Statistics - 10 Experimental Design and Analysis of Variance VIEW

Example text

Because we are comparing the treatments by using the same experimental units, any true differences between the treatments will not be concealed by differences between the experimental units. The last technique we studied in this chapter was two-way analysis of variance (two-way ANOVA). Here we study the effects of two factors by carrying out a two-factor factorial experiment. If there is little or no interaction between the two factors, then we are able to separately study the significance of each of the two factors.

However, the number of observations in each group can be different. • Select MegaStat : Analysis of Variance : One-Factor ANOVA. C6 into the Input range box. • If desired, request “Post-Hoc Analysis” to obtain Tukey simultaneous comparisons and pairwise t tests. 05. • Check the Plot Data check box to obtain a plot comparing the groups. • Click OK in the One-Factor ANOVA dialog box. 7 (page 414) in the arrangement shown in the screenshot. Here each column corresponds to a treatment (in this case, a production method), and each row corresponds to a block (in this case, a machine operator).

4 Two-Way Analysis of Variance is greater than the Fa point based on (a Ϫ 1)(b Ϫ 1) numerator and ab(m Ϫ 1) denominator degrees of freedom. 6, page 818). 05 level of significance. We conclude that little or no interaction exists between shelf display height and shelf display width. That is, we conclude that the relationship between mean demand for the bakery product and shelf display height depends little (or not at all) on the shelf display width. Further, we conclude that the relationship between mean demand and shelf display width depends little (or not at all) on the shelf display height.

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Business Statistics - 10 Experimental Design and Analysis of Variance VIEW


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