Multiple Linear Regression

Summary

Multiple linear regression, on the other hand, extends this concept by including two or more independent variables to predict the dependent variable. This is represented by the equation $y=B_0+B_1x_1+B_2x_2+...+B_nx_n+\epsilon$, where $x_1,x_2,...,x_n$ are the independent variables.

This approach allows for a more complex analysis, capturing the effect of multiple variables on the dependent variable simultaneously. It is particularly useful in scenarios where the dependent variable is influenced by several factors, providing a more nuanced understanding of the relationships at play.

Using R Studio

Duration: 19 minutes 33 seconds (with timestamps).

The following is a video that describes step-by-step how to perform multiple linear regression on R.

See Source Code of sample dataset and R data processing.

Overview

1. Acquiring a dataset (0:27)

2. Generating a Comma Separated Values (CSV) file from Excel (5:15)

3. Basic data cleaning (0:48)

4. Loading data in R Studio (6:38)

5. Performing a Linear Model regression (9:00)

6. Analyzing and Optimizing Linear Model regression performance (11:04 onwards)

Verifying results with Excel

Duration: 2 minutes 57 seconds.

References

• Step-by-step Process from Selecting a Dataset to Building and Optimizing a Regression Model

• GitHub Source Code