What is the purpose of using dummy variables in regression analysis?

The use of dummy variables in regression analysis is primarily to represent categorical data quantitatively. Categorical data, which can include variables like gender, race, or different product types, cannot be directly included in a regression model as numerical values. Dummy variables provide a way to convert these categories into a numerical form that the model can understand. By assigning a binary value (0 or 1) to each category, you can effectively represent the presence or absence of a particular attribute. For example, if you have a categorical variable for "color" with three categories: red, blue, and green, you would create two dummy variables (e.g., one for red and one for blue), while green would be the reference category.

This approach allows regression analysis to easily process and analyze the impact of categorical variables on the dependent variable, thus keeping the model accurate and meaningful. The transformation into dummy variables ensures that important differences among groups can be quantified and analyzed effectively within the regression framework.