DumindUdara/Linear-Regression-Model

Linear regression with dummy variables

Linear regression with dummy variables

Linear regression with dummy variables is a common technique used when dealing with categorical data in regression analysis. Dummy variables are binary variables created to represent different categories or levels of a categorical variable

For example, if you have a categorical variable like "gender" with two levels, male and female, you can create a dummy variable where 1 represents male and 0 represents female (or vice versa). This allows you to incorporate categorical variables into your linear regression model.

Here's a basic outline of how you might develop a linear regression model with dummy variables:

01. Data Preparation

Prepare your dataset, including identifying categorical variables that need to be represented as dummy variables.

02. Create Dummy Variables

For each categorical variable, create dummy variables representing its categories. If you have 𝑛 categories, you typically create (n−1) dummy variables to avoid multicollinearity (the dummy variable trap).

03. Model Building

Once you have your dummy variables, you can include them along with your continuous variables in your linear regression model. The model equation will look something like this

04. Model Training

05. Model Evaluatio

06. Prediction and Inference