diff --git a/exercises/_09-sa-model-logistic.qmd b/exercises/_09-sa-model-logistic.qmd
index 8047fb33..ff1a60c5 100644
--- a/exercises/_09-sa-model-logistic.qmd
+++ b/exercises/_09-sa-model-logistic.qmd
@@ -10,5 +10,5 @@
 1.  \(a\) The variable `exclaim_subj` should be removed, since it's removal reduces AIC the most (and the resulting model has lower AIC than the None Dropped model). (b) The variable `cc` should be removed. (c) Removing any variable will increase AIC, so we should not remove any variables from this set.
 \addtocounter{enumi}{1}
 
-1.  \(a\) The AIC is smallest using the variables `sex`, `head_l`, `skull_w`, `total_l`, and `tail_l` to predict region (AIC = 83.52), so we would choose that model. (b) If the metric is equivalent across two models with different numbers of variables, we usually want the model with smaller number of variables. Sometimes refered to as Occam's razor, the simplest explanation is often the one that will generalize most effectively.
+1.  \(a\) The AIC is smallest using the variables `sex`, `head_l`, `skull_w`, `total_l`, and `tail_l` to predict region (AIC = 83.52), so we would choose that model. (b) If the metric is equivalent across two models with different numbers of variables, we usually want the model with smaller number of variables. Sometimes referred to as Occam's razor, the simplest explanation is often the one that will generalize most effectively.
 \addtocounter{enumi}{1}