What general model can accommodate curvilinear relationships in multiple regression analysis?

The general linear model is well-suited to handle a variety of relationships, including curvilinear ones, within the context of multiple regression analysis. This model extends beyond the constraints of simple linear regression by allowing for transformations of variables or the inclusion of polynomial terms. By introducing these transformations or additional variables, the model can be adapted to capture non-linear patterns in data.

For instance, if the relationship between an independent variable and the dependent variable is quadratic, a general linear model can include a squared term of that independent variable, thereby capturing the curvilinear nature of the relationship. This flexibility makes the general linear model a powerful tool in regression analysis, as it can effectively model complex relationships that are not strictly linear.

In contrast, the simple linear model restricts the relationship to a straight line, making it inadequate for curvilinear patterns. A log-linear model typically involves taking the logarithm of either the dependent variable or one or more independent variables, which also may not accommodate all types of curvilinear relationships. Nonlinear models are specifically designed to handle non-linear relationships, but the general linear model encompasses a broader range of relationships while still maintaining a linear structure in terms of the parameters being estimated.