What characterizes a Multiple Linear Regression Model?

A Multiple Linear Regression Model is characterized by its inclusion of more than one independent variable. This statistical method is used to analyze the relationship between one dependent variable and two or more independent variables. The model aims to predict the outcome of the dependent variable based on the changes or variations in the independent variables, allowing for a more comprehensive understanding of the factors that influence the outcome.

In practice, this model helps researchers and analysts identify the strength and nature of the relationships between variables, assess the impact of multiple predictors, and make informed decisions based on the insights gained from the model's findings. By incorporating multiple independent variables, the model can provide a more nuanced view of the data compared to simple linear regression, which focuses on just one independent variable.