The chi-square test can refer to several different types of tests. Here I will discuss the one-way and two-way tests. The two-way test generalizes to multi-way tests in a natural way. These tests are tests for **nominal** variables (for a discussion of what a nominal variable is, see this post). The one-way test tests whether a variable is distributed according to some proportions that you specify beforehand. The two-way and multi-way tests test whether two (or more) variables are associated.

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When you have bivariate data – that is, data on two variables – either or both may be categorical or continuous. When there is one of each, and you want to compare the distribution of one across levels of the other, a parallel box plot is a good option. Suppose, for example, you want to compare the heights of people across ethnic groups. Read more!

Regression to the mean is a well known statistical artifact affecting correlated data that is not perfectly correlated. It was first noticed by Sir Francis Galton in the late 19th century. He noted that the tallest fathers will have sons who are not as tall, and, similarly, the shortest fathers will have sons who are not as short. But this is true, not because of any general tendency toward mediocrity: Indeed, the range of heights of people shows no signs of diminishing. How can this be? Read more!

This is a talk developed by David Cassell and me, and given at NESUG and SGF and WUSS

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There are many books that teach you to use SAS or that teach you to use R. There is at least one book that teaches R to people who know SAS or SPSS (R for SAS and SPSS users by Robert Muenchen, and it’s very good).