Why are dummy variables used in regression analysis?

Dummy variables play a crucial role in regression analysis by transforming categorical data into a numerical format that can be readily used in statistical models. In many cases, regression analysis requires inputs in a numeric format, but data often includes categorical variables like gender, race, or product types that do not have intrinsic numeric values. By creating dummy variables, which assign a binary value (0 or 1) to each category within those variables, analysts can effectively include categorical data in their models.

For example, if we have a categorical variable such as "Color" with values like "Red", "Blue", and "Green", we can create dummy variables for each of these colors. This allows the regression model to interpret the impact of each color on the dependent variable, facilitating a more accurate and meaningful analysis. The numerical representation that dummy variables provide ensures that categorical data does not misrepresent relationships within the regression model, thereby contributing to a better understanding of the influences being studied.

Other options relate to aspects that are not the primary purpose of dummy variables in regression analysis. Simplifying model outcomes does occur as a byproduct of using numeric values, but the key function is the representation of categorical data. Increasing data processing speed and eliminating the need for data cleaning do not directly pertain