Analyses with linear models assume that the variances of values for every combination of explanatory variable are equal.

Name: Analyses with linear models assume that the variances of values for every combination of explanatory variable are equal.
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This statement refers to the assumption of homoscedasticity, which is a fundamental assumption in linear regression models, indicating that the variance of the residuals (errors) should be constant for all levels of the explanatory variables.

Analyses with Linear Models Assume That the Variances of Values