(*Assuming you don't want to reject the null. On the other hand, if the chi-square you calculated was smaller than the critical value, then theĭata did fit the model, you fail to reject the null hypothesis, and go out and party.* Then the data did not fit the model, which means you have to reject the null hypothesis. So for a test with 1 df (degree of freedom), the "critical" value of theīasically, if the chi-square you calculated was bigger than the critical value in the table, To calculate the degrees of freedom, we subtract 1 from the number of samples (or a number of categories in the sample). Use your df to look up the critical value of the chi-square test, also called the chi-square-crit. As we talkedĪbout on the last page, this is the same as the number of rows in your table minus 1. The first thing you need to know is the degrees of freedom in your test. You can use a chi square table, like the one on the right (books sometimes have a moreĬomplicated table which we'll talk about at the bottom of the page).Īlthough this table does come from a mathematical function (called a chi-squareĭistribution, go figure!) for our purposes you can basically treat it like it We know that when you have a sample and estimate the mean, you have n 1 degrees of freedom, where n is the sample size. For that of independence (in a crosstab table). Once you know the degrees of freedom (or df), I want to use the method of moment generating functions, because I already understand the. For the chi-squared test of goodness of fit, the degrees of freedom is one less than the number of categories.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |