> For the complete documentation index, see [llms.txt](https://www.hultedtech.club/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.hultedtech.club/resources/shared-notes/statistics/regression-analysis/multiple-linear-regression.md).

# 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](https://github.com/darrensapalo/laptop-prices-dataset/tree/main) 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)

{% embed url="<https://www.youtube.com/watch?v=sZIX-v_nRTU>" %}

## Verifying results with Excel

*Duration: 2 minutes 57 seconds.*

{% embed url="<https://www.youtube.com/watch?v=TCy28DGogwg>" %}

## References

* [Step-by-step Process from Selecting a Dataset to Building and Optimizing a Regression Model](https://www.youtube.com/watch?v=sZIX-v_nRTU)
* [GitHub Source Code](https://github.com/darrensapalo/laptop-prices-dataset/tree/main)


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