20251213 - typo

Author: isaactpetersen

multiple-regression.qmd

@@ -391,7 +391,7 @@ Linear regression models make the following assumptions:
 - the residuals are normally distributed
 
 Homoscedasticity of the residuals means that the variance of the residuals does not differ as a function of the [outcome variable](#sec-correlationalStudy) or as a function of the [predictor variable](#sec-correlationalStudy) (i.e., the residuals show constant variance as a function of [outcome](#sec-correlationalStudy)/[predictors](#sec-correlationalStudy)).
-If the residuals differ as a function of the [outcome](#sec-correlationalStudy) or [predictor](#sec-correlationalStudy) variable, this is called heterotoscedasticity.
+If the residuals differ as a function of the [outcome](#sec-correlationalStudy) or [predictor](#sec-correlationalStudy) variable, this is called heteroscedasticity.
 
 Those are some of the key assumptions of multiple regression.
 However, there are additional assumptions of multiple regression, including ones discussed in the chapter on [Causal Inference](#sec-causalInference).