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Clarify the impact of collinearity on ROPE and hypothesis testing.

Author: ethanknights

vignettes/region_of_practical_equivalence.Rmd

@@ -241,7 +241,7 @@ independent, the joint parameter distributions may shift towards or away from
 the ROPE. Collinearity invalidates ROPE and hypothesis testing based on
 univariate marginals, as the probabilities are conditional on independence. Most
 problematic are parameters that only have partial overlap with the ROPE region.
-In case of collinearity, the (joint) distributions  of these parameters may
+In case of collinearity, the (joint) distributions of these parameters may
 either get an increased or decreased ROPE, which means that inferences based on
 ROPE are inappropriate [@kruschke2014doing].
 
@@ -251,6 +251,3 @@ between more than two variables, a first step to check the assumptions of this
 hypothesis testing is to look at different pair plots. An even more
 sophisticated check is the projection predictive variable selection
 [@piironen2017comparison].
-
-
-