# 
## Overview
**Robust regression** is a method we use when we have #key# influential observations: These are the observations that dramatically alter the coefficient estimates of the regression model if removed. #key# or outliers in our dataset. To do this, we use the **`rlm()` method** from the `MASS` library.

## Syntax
```
rlm(formula, data)
```

## Parameters
This method takes two inputs, **`formula`** and **`data`**. 

`formula` is in the form of $y ~ + x_1 + x_2 +...$, where $x_1$ and $x_2$ are variables from the `data` on which we are running the regression model. 


## Example
Let's look at a working example.

     


suppressPackageStartupMessages(library(MASS))

# Lets start by creating a data set.

df <- data.frame(x1=c(1, 3, 3, 4, 4, 6, 6, 8, 9, 3,
                      11, 16, 16, 18, 19, 20, 23, 23, 24, 25),
                 x2=c(7, 7, 4, 29, 13, 34, 17, 19, 20, 12,
                      25, 26, 26, 26, 27, 29, 30, 31, 31, 32),
                  y=c(17, 170, 19, 194, 24, 2, 25, 29, 30, 32,
                      44, 60, 61, 63, 63, 64, 61, 67, 59, 70))
                      
#function call.

rlm(y~x1+x2, data=df)


## Explanation

* Line 1: In order to use the `rlm()` method, we access the `MASS` package by using `library(MASS)` command. The
**`suppressPackageStartupMessages()` method** is used to prevent any additional messages from cluttering our console.

* Lines 5 to 10: We generate our dataset.
  
* Line 14: We call the `rlm()` method to perform regression on the dataset.

chrome.tar.gz

How to perform robust regression in R

Use the rlm() method from the MASS library in R to perform robust regression on a dataset.