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Analysis of Variance (ANOVA)

1 Comparison of Several Treatments—One-Way Analysis of Variance

2 Population Model and Inferences for a One-Way Analysis of Variance

3 Simultaneous Confidence Intervals

4 Graphical Diagnostics and Displays to Supplement ANOVA

5 Randomized Block Experiments for Comparing k Treatments

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Which Brand HDTV Has the Clearest Picture?

A good experimental design is to collect samples of several HDTVs of each brand and measure their picture clarity. The statistical technique called “analysis of variance” enables us to verify differences among the brands.

In Chapter 10, we introduced methods for comparing two population means. When several means must be compared, more general methods are required. We now become acquainted with the powerful technique called analysis of variance (ANOVA) that allows us to analyze and interpret observations from several populations. This versatile statistical tool partitions the total variation in a data set according to the sources of variation that are present. In the context of comparing k population means, the two sources of variation are (1) differences between means or treatments and (2) within population variation (error). We restrict our discussion to this case, although ANOVA techniques apply to much more complex situations.

In this chapter, you will learn how to test for differences among several means and to make confidence ...

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