STAT 302 Lecture Notes - Lecture 1: Relative Risk, Null Hypothesis
23 Apr 2016
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Document Summary
The chi-square goodness-of-fit test allows testing of hypotheses about a categorical variable with two or more levels. In particular, the chi-square goodness-of-fit test allows us to test whether a categorical variable follows a given probability distribution. Expected counts do not have to be round numbers, though. The chi-square statistic is a measure of how far observed counts are from expected counts under the null hypothesis. The formula for the statistic is k i where k is the number of different outcomes the categorical variable can take. Each of the k terms in the sum is called a chi-square component. The chi-square distributions are a family of distributions that take only positive values and are skewed to the right. A specific chi-square distribution is specified by giving its degrees of freedom. The chi-square goodness of fit test involving k outcomes uses critical values from the chi-square distribution with k.
