Logistic Regression with Categorical Data in R

Logistic Regression with Categorical Data in R

Key points Logistic regression is a statistical technique for modeling binary outcomes as a function of one or more explanatory variables, which can be either continuous or categorical. Categorical variables have a finite number of possible values, such as gender, color, or country. They can be either nominal or ordinal and either binary or multi-level. To perform logistic regression in R with categorical variables, we need to create dummy variables for each level of the categorical variable, except for one reference level. A dummy variable is a binary variable that takes one if the observation belongs to a certain level and 0 otherwise. We can use the glm function with family = binomial argument to fit a logistic regression model in R. The glm function returns a model object that contains the estimated coefficients, their standard errors, z-values, and p-values, as well as the model fit statistics, such as the deviance, the AIC, and the number of iterations. We can use the predict functio…