 In our next chapter on regression in Chimovi, we're going to look at associations, specifically through correlation and scatter plots. And we're also going to look at the ways we can use many variables to predict scores on one variable, how we can use predictor variables to look at an outcome or criterion variable. And we'll do this primarily through variations on regression. Chimovi gives us actually a great set of choices. We can do standard linear regression, one of the most powerful, flexible and useful procedures available. We can also do binomial logistic regression where the outcome variable is not a quantitative or continuous score, but a dichotomy, this or that, and you're trying to use a collection of variables to predict which of two categories a case will go into. Then there's multinomial logistic regression, where you have several categories in your outcome variable. This is actually a very sophisticated procedure. And it's a surprising thing that Chimovi includes this and it includes it for free. And then finally, we'll look at ordinal logistic regression recently added to Chimovi, which allows you to use again, a collection of variables to predict which of several ordered categories a case falls into. And between this set, linear regression, binomial multinomial and ordinal logistic regression, we have an extraordinary collection of tools for getting more insight out of our data. And in each case, what we are doing is we're using several variables to predict an outcome on one. I like to think of it as the statistical version of e pluribus unum, a motto of the United States, which means out of many one. And in this case, out of many variables and many data points, one conclusion. And so let's look at the ways that Chimovi let us use an entire collection of regression techniques to explore data and get useful insight out of it.