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We saw that the different treatments are assigned to experimental units, and we discussed the completely randomized experimental design. This design assigns independent, random samples of experimental units to the treatments. We began studying how to analyze experimental data by discussing one-way analysis of variance (one-way ANOVA). Here we study how one factor (having p levels) affects the response variable. In particular, we learned how to use this methodology to test for differences between the treatment means and to estimate the size of pairwise differences between the treatment means.

Click OK in the ANOVA : Two-Factor Without Replication dialog box. • Drag the border of Column B to reveal complete labels for the ANOVA output. 05 in the Alpha box. Click on “Output Range,” and enter a cell location for the upper left corner of the output (here, cell B8) into the “Output Range” box. 3 10. 14. • Select Tools : Data Analysis : ANOVA : TwoFactor With Replication and click OK in the Data Analysis dialog box. C7 into the “Input Range” box. • Enter the value 3 into the “Rows per sample” box (this indicates the number of replications).

0122 cases per month. If we conclude that (substantial) interaction exists between factors 1 and 2, the effects of changing the level of one factor will depend on the level of the other factor. In this case, we cannot separate the analysis of the effects of the levels of the two factors. 2) to compare all of the treatment means (the mij’s) with the possible purpose of finding the best combination of levels of factors 1 and 2. For example, if there had been (substantial) interaction in the shelf display case, we could have used one-way ANOVA to compare the six treatment means—mBR, mBW, mMR, mMW, mTR, and mTW—to find the best combination of display height and width.

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Business Statistics - 10 Experimental Design and Analysis of Variance VIEW

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