In regression analysis, a dummy variable (also known as indicator or bound variable) is one that takes the values 0 or 1 to indicate the absence or presence of some categorical effect that may be expected to shift the outcome. In statistics regression analysis is a collective name for techniques for the modeling and analysis of numerical data consisting of values of a Dependent variable (response For example, in econometric time series analysis, dummy variables may be used to indicate the occurrence of wars, or major strikes. Econometrics is concerned with the tasks of developing and applying Quantitative or Statistical methods to the study and elucidation of economic principles In Statistics, Signal processing, and many other fields a time series is a sequence of Data points measured typically at successive times spaced at (often Strike action, often simply called a strike, is a work stoppage caused by the mass refusal by Employees to perform work. Use of dummy variables usually increases model fit (coefficient of determination), but at a cost of fewer degrees of freedom and loss of generality of the model. In Statistics, the coefficient of determination, R 2, is the proportion of variability in a data set that is accounted for by a statistical model Too many dummy variables result in a model that does not provide any general conclusions.

Dummy variables may be extended to more complex cases. For example, seasonal effects may be captured by creating dummy variables for each of the seasons. In panel data fixed effects estimator dummies are created for each of the units in cross-sectional data (e. In Statistics and Econometrics, the term panel data refers to two-dimensional data In Econometrics and Statistics the fixed effects estimator (also known as the within estimator) is an Estimator for the Coefficients Cross-sectional data in Statistics and Econometrics is a type of one-dimensional Data set. g. firms or countries) or periods in a pooled time-series. However in such regressions either the constant term has to be removed, or one of the dummies. In Mathematics, the constant term of a Polynomial is the term of degree 0

When there are dummies in all observations, the constant term has to be excluded. Observation is either an activity of a living being (such as a Human) which senses and assimilates the Knowledge of a Phenomenon, or the recording of data If a constant term is included in the regression, it is important to exclude one of the dummy variables from the regression, making this the base category against which the others are assessed. If all the dummy variables are included, their sum is equal to 1 (which stands for the variable X₀ to the constant term B₀), resulting in perfect multicollinearity. Multicollinearity is a statistical phenomenon in which two or more predictor variables in a Multiple regression model are highly correlated This is referred to as the dummy variable trap.

See also

• Free variables and bound variables
• Indicator function
• Interaction variable
• Calculation of glass properties - Dummy variables for detection of systematic errors