Probit regression is used to model binary response variables. It can be used in cases where Logit regression is applicable.

## How To Model Probit Regression?

The synax for probit is similar to logit, except for a variation in family parameter.

`probitModel <- glm (binaryResponse ~ pred1, pred2, data=inputData, family=binomial(link="probit")) # probit`

summary (probitModel) # Model summary

confint (probitModel) # confidence intervals.

As in logistic regression, the co-efficient values should both be positive or negative. If the condidence range includes 0, then it is possible that the coeffient value could take on the value 0, which will effective the same as not including the variable in the model.

`predict(myprobit, newData, type = "response", se.fit = TRUE) # predict on test data`

logLik(probitModel) # log likelyhood statistic