Statistical Tableau

Chapter 14. Understanding Multiple Linear Regression in R and Python

In Chapter 8, you were introduced to simple linear regression. This model is used to analyze the relationship between an independent variable and a single dependent variable. However, there are many times when several variables together may cause an effect, but individually, those effects may not be seen.

In this chapter, you will learn what multiple linear regression is and its equation, and you will implement some high-level examples. You will also use R and Python scripts to learn how to implement the model in both languages. In Chapter 15, you will practice using these scripts to begin implementing more advanced models in Tableau using external connections.

What Is Multiple Linear Regression?

Multiple linear regression is a statistical method used to analyze the relationship between multiple independent variables and a dependent variable. In simple linear regression, there is only one independent variable used to predict the dependent variable. However, in multiple linear regression, two or more independent variables are employed to forecast or understand changes in the dependent variable.

Multiple linear regression has many applications in business when you are trying to use multiple inputs to understand something. A few examples of its application are as follows:

Sales forecasting

Businesses often use multiple linear regression to predict sales based on various factors such as advertising expenditure, ...