Which of the following statements is true about multiple regression analysis?

The correct statement regarding multiple regression analysis is that it allows for several independent variables. This feature is central to multiple regression, where the goal is to understand the relationship between one dependent variable and two or more independent variables. By incorporating multiple predictors, analysts can gain insights into complex relationships and how various factors contribute to the outcome being studied.

In practice, multiple regression enables the assessment of how different independent variables simultaneously affect the dependent variable, which is essential for modeling real-world scenarios where many factors are interconnected. This flexibility is a significant advantage of multiple regression over simple linear regression, which is limited to a single independent variable.

The other statements do not accurately reflect the characteristics of multiple regression: it indeed can involve multiple independent variables rather than being restricted to just one; it typically predicts continuous outcomes rather than categorical ones, which would require logistic regression; and while the basic form of multiple regression is linear, it can also incorporate non-linear relationships through transformations or polynomial terms.