Which type of regression model includes second-order terms for predictor variables?

The option indicating the second-order model is correct because this type of regression explicitly includes squared terms of the predictor variables in its formulation. By adding these second-order terms, the model is able to capture non-linear relationships between the independent and dependent variables, allowing for a more accurate representation of the data when such relationships exist.

In contrast, a simple linear model focuses solely on individual predictor variables without incorporating any polynomial terms like squares or interactions. Multiple regression can include multiple independent variables, but it does not necessarily mean that it includes second-order terms unless specifically specified. Logistic regression is used primarily for modeling binary outcomes and does not incorporate terms in the same way as polynomial regression models do.

Thus, among the options provided, the second-order model is the one that inherently includes these squared terms and is clearly designed for analyzing such relationships.