<P> In statistics and econometrics, particularly in regression analysis, a dummy variable (also known as an indicator variable, design variable, Boolean indicator, categorical variable, binary variable, or qualitative variable) is one that takes the value 0 or 1 to indicate the absence or presence of some categorical effect that may be expected to shift the outcome . Dummy variables are used as devices to sort data into mutually exclusive categories (such as smoker / non-smoker, etc .). For example, in econometric time series analysis, dummy variables may be used to indicate the occurrence of wars or major strikes . A dummy variable can thus be thought of as a truth value represented as a numerical value 0 or 1 (as is sometimes done in computer programming). </P> <P> Dummy variables are "proxy" variables or numeric stand - ins for qualitative facts in a regression model . In regression analysis, the dependent variables may be influenced not only by quantitative variables (income, output, prices, etc .), but also by qualitative variables (gender, religion, geographic region, etc .). A dummy independent variable (also called a dummy explanatory variable) which for some observation has a value of 0 will cause that variable's coefficient to have no role in influencing the dependent variable, while when the dummy takes on a value 1 its coefficient acts to alter the intercept . For example, suppose membership in a group is one of the qualitative variables relevant to a regression . If group membership is arbitrarily assigned the value of 1, then all others would get the value 0 . Then the intercept (the value of the dependent variable if all other explanatory variables hypothetically took on the value zero) would be the constant term for non-members but would be the constant term plus the coefficient of the membership dummy in the case of group members . </P> <P> Dummy variables are used frequently in time series analysis with regime switching, seasonal analysis and qualitative data applications . Dummy variables are involved in studies for economic forecasting, bio-medical studies, credit scoring, response modelling, etc . Dummy variables may be incorporated in traditional regression methods or newly developed modeling paradigms . </P>

When do we use dummy variables in regression