solve #8

Author: jiapivialiu

_freeze/flatline-forecaster/execute-results/html.json

@@ -6,8 +6,6 @@ The flatline forecaster is a very simple forecasting model intended for `epi_df`
 
 ```{r}
 #| echo: false
-#| message: false
-#| warning: false
 source("_common.R")
 ```
 
@@ -27,8 +25,7 @@ jhu
 
 ### The basic mechanics of the flatline forecaster
 
-The simplest way to create and train a flatline forecaster to predict the d
-eath rate one week into the future, is to input the `epi_df` and the name of 
+The simplest way to create and train a flatline forecaster to predict the death rate one week into the future, is to input the `epi_df` and the name of 
 the column from it that we want to predict in the `flatline_forecaster` function.
 
 ```{r}

_freeze/flatline-forecaster/figure-html/unnamed-chunk-12-1.svg

@@ -2,7 +2,6 @@
 
 ```{r}
 #| echo: false
-#| warning: false
 source("_common.R")
 ```
 
@@ -93,7 +92,7 @@ contains the number of confirmed cases and deaths from June 4, 2021 to
 Dec 31, 2021 in some U.S. states. 
 
 We wish to predict the 7-day ahead death counts with lagged cases and deaths.
-Furthermore, we will let each state be a dummy variable. Using differential 
+Furthermore, we will include the categorical variable, *states*, as dummy variables. Using different 
 intercept coefficients, we can allow for an intercept shift between states.
 
 One possible model takes the form

_freeze/flatline-forecaster/figure-html/unnamed-chunk-14-1.svg

@@ -2,7 +2,6 @@
 
 ```{r}
 #| echo: false
-#| warning: false
 source("_common.R")
 ```
 

_freeze/preprocessing-and-models/execute-results/html.json

@@ -2,7 +2,6 @@
 
 ```{r}
 #| echo: false
-#| warning: false
 source("_common.R")
 ```
 

_freeze/sliding-forecasters/execute-results/html.json

@@ -2,7 +2,6 @@
 
 ```{r}
 #| echo: false
-#| warning: false
 source("_common.R")
 ```
 
@@ -256,7 +255,7 @@ To read more about formula syntax, see `formula()`.
 
 Similar to how we were able to add an interaction term using recipes, we can also perform a transformation as a pre-processing step. The function used for this is `step_mutate()` (which acts like `dplyr::mutate()`).
 
-Note that, in general, if you are specifying a recipe aim to keep as much of the pre-processing in your recipe specification as possible. This helps to ensure that the transformation will be applied to new data consistently.
+Note that, in general, when specifying a recipe, we aim to keep as much of the pre-processing in the recipe specification as possible. This helps to ensure that the transformation will be applied to new data consistently.
 
 ```{r}
 rec_spec_pow2 <- recipe(bill_length_mm ~ bill_depth_mm, data = penguins) %>%