 Perhaps the quickest way to get some insight into your data is to do basic descriptive statistics like frequencies and means and standard deviations. Fortunately, that's really easy to do in Jmovi. Now the data set that I have open here is the bugs data set, where we have a number of people, and they are rating how much they would like to get rid of bugs that are either high or low disgusting or in high or low frightening. And we have the people's gender, region and education. And all we need to do is come over here to exploration and click descriptives. Now I've installed the scatter modules, so these two menus show up here. If you don't have those, that's not a problem. Just hit descriptives. And then we get to pick the variables we want. And Jmovi will either give us means and so on or frequencies. Now, I find it useful to start with the predictor variables, the ones that you're going to use to predict the outcomes. And in this case, that's going to be these three categorical demographic things, gender, region and education. So I'm going to put those over here into variables. And what we're going to get is a table that really only tells us how many cases there are. There's 91 or 92, and we're missing one or two on each of them. I don't need these other variables, because these have to do with quantitative or continuous variables. So I'm just going to remove those for right now. On the other hand, because I have nominal or categorical variables, it would be nice to get frequency tables. So I'm going to click this selection right here. And then it's going to automatically expand. And it's going to give me the count the number of people in each category, along with the percentage of the total data and the cumulative percent. And so for instance, we can see that we've got about two thirds women and one third men. And that we've got a lot of people from North America, and almost 10% from here, but other groups are pretty small. And level of education, we have a spike at less and we have 15 people or 16.5% at high, which I assume means high school. Anyhow, this is the first step get some basic demographics or the things that you're going to use as predictors. Now, I also want to look at the outcome variables which are scaled, they're measured on a one to 10 or zero to 10 scale. And I'm going to do the descriptives command over again. I'm going to hit descriptives, and then it shows up as blank. And what I'm going to do is I'm going to take these four outcomes and put those in the variables over here. Now this time, because these are scaled or quantitative outcomes is going to give me mean median and so on. On the other hand, there's a few that I should add to that. Most importantly is the standard deviation. That's sort of a bare minimum for how spread out things are. It's also nice to have the quartiles. Now if you want to, you can add the standard error, the mean or the variant and you can get skewness and kurtosis each with a standard error. But this what I have right here is usually plenty. By the way, you may notice that this table strongly resembles the command that you use in SPSS to get basic descriptive statistics. That's on purpose. Jmovi is modeled to be friendly to people who are migrating from SPSS. And then it brings in the power of R. But it's designed to be accessible. And so now we have some descriptive statistics on the categorical demographic variables. We have some descriptive statistics on the quantitative or continuous outcomes. There is one other thing that we can do here that's worth mentioning. And that is, we may want to look at some of these variables here, the outcome variables and break them down by one of our other categories. The only one that's going to work really well here is male or female. The general rule of thumb with quantitative variables is you want to have at least 10% of your sample in the smallest group. So we would probably have to combine people to be sort of North America or other. But I don't know that would make a lot of sense. But what I can do is I can get the statistics for these four outcome variables, how much people want to get rid of these various bugs in terms of how disgusting or frightening they are. And I can break that down by gender. To do that, I come back up to exploration and go to descriptives. I pick the four outcome variables, which again stand for like low disgusting, low frightening, low disgusting, high frightening and so on. And then I get our nominal or categorical variable gender, which is text in this case, because they actually wrote out male or female. And then I put them down here. And then I'm going to come and make a few changes to the table that I'm going to get. Mostly, I'm going to remove some of these statistics because when you start breaking it down by other categories, it gets really busy. And so I'm just going to get the N, the mean, and the standard deviation. And that's probably going to be sufficient for what I'm doing right now. And here you can see how it broke it down into those categories can click out here. And so that is a quick run through of the descriptive statistics that you can get quickly and easily in Jmovi. Again, a very good first look at your data and a way to get started on understanding what you have to help you shape and then interpret your analyses.