Which of the following is not true regarding linear regression?

Linear regression is a statistical method primarily used to model the relationship between a dependent variable and one or more independent variables. The correct assertion in this context is that there can be multiple independent variables, which allows for a more complex and nuanced prediction than simple linear regression, which involves only one independent variable.

The model inherently focuses on predicting a single dependent variable. When linear regression is utilized, the assumption is that the relationship can be expressed through a mathematical equation where the dependent variable is modeled as a function of the independent variables. Thus, the assertion that there can be multiple dependent variables is not true in the context of conventional linear regression. This limitation stems from the fundamental goal of modeling the impact of variates on a single outcome measure, allowing analysts to derive insights and make predictions based on the behavior of that single dependent variable, influenced by one or multiple independent factors.

In summary, linear regression can incorporate multiple independent variables, making it a flexible predictive tool, but it is designed to relate those to a single dependent variable.