 In this section on analyzing frequencies in Jamovie, we get a new way to deal with data. Specifically, we are now dealing with counting, as opposed to measuring. Now measuring means when you say how long does it take to do something or what's the average score here. Counting is like counting the number of rocks, where you're able to enumerate the cases and put the frequencies in as your data. And this requires a different approach. In Jamovie, we have several options for analyzing frequencies. The first and simplest is what's called the binomial test binomial means two names. And it's for when you have two categories of outcomes, like flipping a coin and getting the number of heads and the number of tails. If you have more than two outcomes, say, for instance, you have baskets of several different kinds of mushrooms, you can use the chi squared goodness of fit test to see how things are distributed across those multiple categories. If you want to look at two categories at once and look at the association and say, for instance, you want to look at something like the number of World Cup teams from different countries and break it down by male or female, you know, because we know that in soccer, the American women have done fabulously well. The American men didn't qualify for the World Cup. Most recently, that's looking at an association between two categories. And you can analyze that with what's called the chi squared test of association. You also have something called McNamara's test. This is a relatively unusual one. It's for looking at tables where you have related data, paired observations like data from twins, because they go together, and you have to take together that consideration. Also, if you did the same person at time one and time two, then the frequencies have this built in association and you need to compensate for that McNamara's test lets you do that. And then finally, there's also log linear regression, it accomplishes a lot of the same things, but it uses a different mathematical approach. It's based on regression. There's some pretty serious equations that go into it. But it is often a very flexible and very powerful technique for modeling the number of cases, the observations or frequencies within different cells of your data. And so taken together, this gives us a great range of options for a data analysis for counting and frequencies in Jmovi.