A variable used to model the effect of categorical independent variables in a regression model which generally takes only the value zero or one is called dummy variable.
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A dummy variable is a numeric variable that represents categorical data, such as gender, race, political affiliation, etc. Each dummy variable represents one category of the explanatory variable and is coded with 1 if the case falls in that category and with 0 if not.
In dummy variables, we don't need to write out separate equation models for each subgroup because they enable us to use a single regression equation to represent multiple groups. It is an artificial variable created to represent an attribute with two or more distinct categories/levels.
A dummy variable can take values either 1 or 0. It can express either a binary variable or a categorical variables. The number of dummy variables we must create is equal to k - 1, where k is the number of different values that the categorical variable can take on.
Hence we can conclude that a variable used to model the effect of categorical independent variables in a regression model which generally takes only the value zero or one is called dummy variable.
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