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Logistic regression is primarily used to predict the probability of a categorical dependent variable. This method is particularly suited for scenarios where the outcome is binary, meaning there are two possible outcomes, such as "yes" or "no", "success" or "failure", or "1" and "0".

The logistic regression model estimates the probability that a given input point belongs to a particular category. This is accomplished using a logistic function, which transforms the output of a linear combination of the input features into a value between 0 and 1, making it interpretable as a probability.

In cases where there are more than two categories, extensions of logistic regression, such as multinomial logistic regression, can be employed to handle multi-class classification problems. Thus, its capability to provide probabilities for categorical outcomes underscores why logistic regression is the choice for predicting categorical dependent variables.

The other choices do not align with the primary use of logistic regression; continuous dependent variables would be better suited for linear regression, independent variables are predictors rather than outcomes, and ordinal variables would require different methods that account for the order between the categories.