Binary logistic regression refers to a logistic regression in which the outcome variable has how many categories?

Binary logistic regression refers to a logistic regression in which the outcome variable has how many categories?

• A. Three categories
• B. More than two categories
• C. One category
• D. Two categories

Answer: (D)

Binary logistic regression is used when the dependent variable is dichotomous—two possible outcomes (for example, success vs. failure, yes vs. no). The model estimates the probability of one category as a function of the predictors, using the logistic function to keep predicted probabilities between 0 and 1 and interpreting coefficients in terms of log-odds. If the outcome had three or more categories, you’d turn to multinomial logistic regression (for nominal categories) or ordinal logistic regression (for ordered categories). If there’s only one category, there’s no variation to model.

Binary logistic regression is used when the dependent variable is dichotomous—two possible outcomes (for example, success vs. failure, yes vs. no). The model estimates the probability of one category as a function of the predictors, using the logistic function to keep predicted probabilities between 0 and 1 and interpreting coefficients in terms of log-odds. If the outcome had three or more categories, you’d turn to multinomial logistic regression (for nominal categories) or ordinal logistic regression (for ordered categories). If there’s only one category, there’s no variation to model.