knit -> render

Author: malcolmbarrett

exercises/05-pscores-exercises.qmd

@@ -21,7 +21,7 @@ We are interested in examining the relationship between whether there were "Extr
 
 Below is a proposed DAG for this question.
 
-*Knit this document to see the DAG or refer to the slides*.
+*Render this document to see the DAG or refer to the slides*.
 
 ```{r}
 set.seed(1234)
@@ -83,7 +83,7 @@ Here's a data dictionary of the variables we need in the `seven_dwarfs` data set
 
 ## Your Turn
 
-*After updating the code chunks below, change `eval = TRUE` before knitting.*
+*After updating the code chunks below, change `eval: true` before rendering*
 
 Now, fit a propensity score model for `extra_magic_morning` using the above proposed confounders.
 

exercises/06-pscores-using-exercises.qmd

@@ -33,7 +33,7 @@ seven_dwarfs_prop <- propensity_model |>
 
 ## Your Turn 1 (Matching)
 
-_After updating the code chunks below, change `eval = TRUE` before knitting._
+*After updating the code chunks below, change `eval: true` before rendering*
 
 Create at "matched" data set using the same propensity score model as above and a caliper of 0.2.
 
@@ -52,7 +52,7 @@ matched_df <- ___(matched_dwarfs)
 
 ## Your Turn 2 (Weighting)
 
-_After updating the code chunks below, change `eval = TRUE` before knitting._
+*After updating the code chunks below, change `eval: true` before rendering*
 
 Add the ATE weights to the data frame, `seven_dwarfs_prop`
 

exercises/07-pscores-diagnostics-exercises.qmd

@@ -35,7 +35,7 @@ seven_dwarfs_ps <- propensity_model |>
 
 ## Your Turn 1
 
-_After updating the code chunks below, change `eval = TRUE` before knitting._
+*After updating the code chunks below, change `eval: true` before rendering*
 
 Calculate the standardized mean differences with and without weights
 

exercises/08-outcome-model-exercises.qmd

@@ -19,7 +19,7 @@ We are interested in examining the relationship between whether there were "Extr
 
 ## Your turn
 
-_After updating the code chunks below, change `eval = TRUE` before knitting._
+*After updating the code chunks below, change `eval: true` before rendering*
 
 Create a function called `ipw_fit` that fits the propensity score model from Exercise 03, incorporates the ATE weights calculated in Exercise 04, and fits a weighted outcome model.