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Going One-Way with
Analysis of Variance
In This Chapter
Extending the t-test for comparing two means by using ANOVA
Discovering and utilizing the ANOVA process
Carrying out an F-test
Navigating the ANOVA table Chapter 9
ne of the most commonly used statistical techniques at the interme-
Odiate level is analysis of variance (affectionately known as ANOVA).
Because the name has the word variance in it, you may think that this tech-
nique has something to do with variance — and you would be right. Analysis
of variance is all about examining the amount of variability in a y (response)
variable and trying to understand where that variability is coming from.

One way that you can use ANOVA is to compare several populations regard-
ing some quantitative variable, y. The populations you want to compare
constitute different groups (denoted by an x variable), such as political affilia-
tions, age groups, or different brands of a product. ANOVA is also particularly
suitable for situations involving an experiment where you apply certain treat-
ments (x) to subjects, and you measure a response (y).
In this chapter, you start with the t-test for two population means, the precur-
sor to ANOVA. Then you move on to the basic concepts of ANOVA: sums of
squares, the F-test, and the ANOVA table. You apply these basics to the one-
factor or one-way ANOVA, where you compare the responses based only on
one treatment variable. (In Chapter 11, you can see them applied to a two-
way ANOVA, which has two treatment variables.)