diff --git a/vignettes/check_model.Rmd b/vignettes/check_model.Rmd
index 1bd8e6eee..bf933e13c 100644
--- a/vignettes/check_model.Rmd
+++ b/vignettes/check_model.Rmd
@@ -1,6 +1,6 @@
 ---
 title: "Checking model assumption - linear models"
-output: 
+output:
   rmarkdown::html_vignette:
     toc: true
 tags: [r, performance, r2]
@@ -8,7 +8,7 @@ vignette: >
   \usepackage[utf8]{inputenc}
   %\VignetteIndexEntry{Checking model assumption - linear models}
   %\VignetteEngine{knitr::rmarkdown}
-editor_options: 
+editor_options:
   chunk_output_type: console
 ---
 
@@ -214,6 +214,8 @@ There are several ways to address heteroscedasticity.
 
 3. Transforming the response variable, for instance, taking the `log()`, may also help to avoid issues with heteroscedasticity.
 
+4. Weighting observations is another remedy against heteroscedasticity, in particular the method of [weighted least squares](https://online.stat.psu.edu/stat501/lesson/13/13.1).
+
 ## Influential observations - outliers
 
 Outliers can be defined as particularly influential observations, and this plot helps detecting those outliers. Cook's distance (_Cook 1977_, _Cook & Weisberg 1982_) is used to define outliers, i.e. any point in this plot that falls outside of Cook's distance (the dashed lines) is considered an influential observation.