For logistic regression, what can the predicted probabilities help determine?

The predicted probabilities in logistic regression are valuable because they directly indicate the likelihood of a certain event occurring, particularly in binary classification scenarios. In logistic regression, the model estimates the probability that a given input point belongs to a specific class based on its features.

For instance, if you are using logistic regression to predict whether a customer will purchase a product (yes or no), the predicted probability reflects how confident the model is about the likelihood of that customer making a purchase. This information is crucial for decision-making processes, as it allows for risk assessment and targeted marketing strategies based on these probabilities.

In contrast, the other options do not align with the core purpose of logistic regression. The method is not designed for predicting continuous outcomes directly, determining means of inputs, or calculating standard deviations of outcomes. Its fundamental utility lies in transforming input data into probabilities that help assess event occurrence, which is exactly why option B is the correct choice.