What is a key advantage of using logistic regression over linear probability models?

The key advantage of using logistic regression over linear probability models lies in its ability to handle binary outcome variables effectively, making the second option the most fitting. Logistic regression produces probabilities that are bounded between 0 and 1, which is critical when dealing with categorical data outcomes.

In contrast, linear probability models can predict values outside this range, leading to nonsensical results. Additionally, logistic regression utilizes the logistic function, creating a non-linear relationship between the independent variables and the log-odds of the dependent variable. This non-linear transformation allows for a more accurate fitting of the data in scenarios where the relationship is not strictly linear, thus enhancing predictive power.

Logistic regression also provides a way to manage the peculiarities of categorical data much more gracefully than linear models, particularly when it comes to assessing the effects of independent variables on a binary response variable. Thus, it is this nuanced relationship modeling capability that makes logistic regression preferable over linear probability models for categorical outcomes.