Which problem is best modeled using logistic regression?

Logistic regression is particularly suited for problems where the outcome variable is binary, meaning there are two possible discrete outcomes. In the context of identifying individuals likely to respond positively to advertisements, this scenario involves classifying individuals into two categories: those who will respond positively and those who will not. Logistic regression can provide the probability that a given individual falls into one of these two categories based on predictor variables such as demographics, previous purchase behavior, or engagement with past advertisements.

This model excels in situations where the goal is to assess the likelihood of an event occurring and is essential in fields like marketing to inform targeted advertising strategies. The use of logistic regression here enhances the understanding of factors influencing response rates, thus aiding in decision-making processes related to marketing efforts.

In contrast, the other options involve different types of outcomes. Predicting a stock's price involves continuous outcomes rather than binary. Similarly, predicting the number of touchdowns scored is a count variable that would typically utilize count regression methods. Predicting a person's wage as a continuous outcome based on age and education would not suit the logistic model, as it seeks to explain a numerical response rather than categorize outcomes into two classes.