diff --git a/_episodes_rmd/02-high-dimensional-regression.Rmd b/_episodes_rmd/02-high-dimensional-regression.Rmd
index 0eeb0205..9d92c60a 100644
--- a/_episodes_rmd/02-high-dimensional-regression.Rmd
+++ b/_episodes_rmd/02-high-dimensional-regression.Rmd
@@ -221,11 +221,11 @@ to help us understand how ageing manifests.
 
 Using linear regression, it is possible to identify differences like
 these. However, high-dimensional data like the ones we're working with
-require some special considerations. A primary consideration, as we saw
+require some special considerations. A first consideration, as we saw
 above, is that there are far too many features to fit each one-by-one as
 we might do when analysing low-dimensional datasets (for example using
 `lm` on each feature and checking the linear model assumptions). A
-secondary consideration is that statistical approaches may behave
+second consideration is that statistical approaches may behave
 slightly differently in very high-dimensional data, compared to
 low-dimensional data. A third consideration is the speed at which we can
 actually compute statistics for data this large -- methods optimised for