![]() So um I put in my area which is my alpha value, my degrees of freedom is five and then my degrees of freedom for the denominator is 30 and That gives me my critical value. I'm not going to show you how to how to write the program. But I use a calculator and I wrote a program in here called inverse. So 30 is your degrees of freedom for the denominator. So there were 36 data values minus the six metropolitan areas. And then for the denominators, the total number of data values minus the number of categories. So there were six cities that we looked at our metropolitan areas, so 6 -1° of freedom would be five for the numerator. And the way you find that Is the degrees of freedom for the numerator is the number of categories -1. Then you need the degrees of freedom for the numerator and the degrees of freedom for the denominator. The first thing is your alpha value, your significance level and that's usually given to you the problem and that's. The second step is to find the critical value and you can do that using either software or a table, But they're essentially three things you need. ![]() And then the alternative is that at least one of them is different. Uh So the null hypothesis is that all the means are the same. ![]() So there are six metropolitan areas, I think it goes Chicago, Dallas Miami, Denver san Diego and Seattle. So the alternative or the null hypothesis is that all the means are the same. The following is a nova test based on the mean salaries for different metropolitan areas. ![]()
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