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Analysis of covariance using the regression model

Abstract

Regression analysis typically involves the use of continuous independent variables to predict a continuous dependent variable. It is also possible, of course, to use categorical independent variables, in the form of dummy variables, to perform an analysis of variance using the regression model. It should come as no surprise, then, to learn that a regression model can include both continuous and categorical independent variables at the same time. This procedure is known as analysis of covariance. Analysis of covariance is a multiple regression model that contains both continuous independent variables and categorical independent variables represented by dummy variables. According to the conventions of analysis of covariance, the categorical variables are referred to as factors, whereas the continuous variables are referred to as covariates.

Keywords

Regression Model Effect Variable Multiple Regression Model Covariance Model Interaction Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Press, New York 1997

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