To grasp the concept of multiple linear regression, it’s essential to first understand the simple linear model, which we covered in our third edition of the “Making Statistical Concepts Accessible” series.
You can review it via the following link:
Simple Linear Regression – Edition 3
What Sets Multiple Linear Regression Apart? (compared to the simple linear Regression)
Like the simple linear model, multiple linear regression deals with a numeric dependent variable, also called the regressand. The key difference lies in the predictors:
Simple Linear Regression: Includes only one predictor (independent variable or regressor).
Multiple Linear Regression: Involves multiple predictors, making it more versatile in real-world applications where outcomes are often influenced by several factors.
For those interested in the mathematical representation, a linear regression model is expressed as:
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