So far, you have worked with numerical variables. But many times, you will have non-numeric variables in the data sets. These variables are also known as categorical variables. Obviously, these variables cannot be used directly in the model, as they are non-numeric.

Let’s see how you can deal with these variables in the following video.

When you have a categorical variable with, say, ‘n’ levels, the idea of dummy variable creation is to build ‘n-1’ variables, indicating the levels. For a variable, say, ‘Relationship’ with three levels, namely, ‘Single’, ‘In a relationship’, and ‘Married’, you would create a dummy table like the following

Relationship Status | Single | In a Relationship | Married |

Single | 1 | 0 | 0 |

In a Relationship | 0 | 1 | 0 |

Married | 0 | 0 | 1 |

As you can clearly see, there is no need to define **three** different levels. If you drop a level, say, ‘Single’, you will still be able to explain the three levels.

Let’s drop the dummy variable ‘Single’ from the columns and see what the table looks like:

Relationship Status | In a Relationship | Married |

Single | 0 | 0 |

In a Relationship | 1 | 0 |

Married | 0 | 1 |

If both the dummy variables, i.e., ‘In a relationship’ and ‘Married’, are equal to zero, it means that the person is single. If ‘In a relationship’ is denoted by 1 and ‘Married’ by 0, it means that the person is in a relationship. Finally, if ‘In a relationship’ is denoted by 0 and ‘Married’ by 1, it means that the person is married.

Before you move on to the next segment, there’s one concept that needs to be addressed: the concept of scaling the variables. Rahim had addressed scaling when answering a few common doubts regarding linear regression in this optional segment. But now that you have dummy variables too in the picture, let’s revisit the different aspects of scaling.

Note that **scaling just affects the coefficients** and none of the other parameters, such as t-statistic, F-statistic, p-values and R-squared.

Two major methods are employed to scale the variables: standardisation and MinMax scaling. Standardisation brings all the data into a standard normal distribution with mean 0 and standard deviation 1. MinMax scaling, on the other hand, brings all the data in the range of 0-1. The formulae used in the background for each of these methods are as given below:

- Standardisation: x=x−mean(x)sd(x)
- MinMax Scaling: x=x−min(x)max(x)−min(x)