This blog post will teach us how to use probit regression in R, a statistical modeling technique for analyzing binary response variables. Probit regression can be useful when the outcome variable is dichotomous. That is when the outcome variable takes only two possible values, such as “success” or “failure,” “yes” or “no,” or “tinnitus” or “no tinnitus.”
We will start by exploring examples of when to use probit regression in R. Then, we will look at other alternatives and compare them to probit regression. We will also provide an example dataset that we can use to illustrate how to perform probit regression in R.
Once we have a dataset, we will dive into the syntax of building a probit model in R, explaining each step. We will also provide a complete example of probit regression in R to see how it fits together.