Analysis of covariance (ANCOVA) is a combination of analysis of variance (ANOVA) and regression analysis because the model contains both quantitative and qualitative independent variables. The idea is to enhance the ANOVA model by adding one or more quantitative independent variables that are related to the dependent variable. These variables are called concomitant variables or covariates. Increasing the precision of the model results in reducing the error terms. Without a covariate, the error mean square may be so high that a simple ANOVA may not detect differences between treatments. Covariates can also be used to remove the effect of an extraneous variable from the dependent variable. An extraneous variable influences the outcome of an experiment but is not of particular interest to the researcher.

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