From the course: Complete Guide to R: Wrangling, Visualizing, and Modeling Data

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Predicting outcomes with logistic regression

Predicting outcomes with logistic regression

From the course: Complete Guide to R: Wrangling, Visualizing, and Modeling Data

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Predicting outcomes with logistic regression

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- [Instructor] The most common versions of regression assume you have a quantitative, a scaled or measured variable for your outcome. Say for instance, a person's age or their GPA, but you can also do regression when you have a categorical outcome. And in that case, you're going to use something called logistic regression, the most common of which is binomial logistic regression, which means you're trying to predict a yes no, a true false, two possible outcomes on a category. And you're using several different variables to model that. To demonstrate how this works, I'm going to start by loading a few packages including broom, which is for tidying tables, and skimmer, which has some additional functions for descriptives. Neither of those is essential for doing logistic regression, but they have some extra functions that you may want to use in your own analyses, so I'm going to load those. And then I'm going to come down, and I'm going to load the big five data set that we've saved as…

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