Logistic regression predicts the probability of a categorical outcome using the odds ratio. It transforms the probability into the logit (the natural log of the odds), allowing probabilities between 0 and 1 to be modeled. The odds ratio indicates how the odds of the outcome change with a one-unit increase in the predictor. Logistic regression uses maximum likelihood estimation to calculate coefficients that best predict outcomes based on the predictor variables.