Ace the AI Engineering Exam 2026 – Transform Your Tech Dreams into Reality!

Simple regression is characterized by its use of a single independent variable to estimate the value of one dependent variable. This method is typically employed when the relationship between the two variables is being studied in isolation, allowing for a clearer understanding of how changes in the independent variable impact the dependent variable.

In contrast, multiple regression involves the use of two or more independent variables to predict the value of one dependent variable. This allows researchers to account for the influence of multiple factors simultaneously, which can lead to more accurate predictions and a better understanding of complex relationships within the data.

The correct choice emphasizes that simple regression focuses on estimating a single dependent variable through just one independent variable, highlighting its simplicity in comparison to multiple regression's more complex structure of incorporating several independent variables at once.