Final tweaks to stats, sorry to push to main but it's kinda last minute

modules/Statistics/Statistics.Rmd

@@ -638,6 +638,11 @@ binom_fit <- glm(DieselPM_level ~ PovertyPctl_level, data = calenviroscreen, fam
 summary(binom_fit)
 ```
 
+## Logistic Regression
+
+See this [case study](https://www.opencasestudies.org/ocs-bp-vaping-case-study/#Logistic_regression_%E2%80%9Cby_hand%E2%80%9D_and_by_model) for more information.
+
+
 ## Odds ratios
 
 > An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure.
@@ -663,10 +668,17 @@ calenviroscreen <-
 
 response <- calenviroscreen %>% pull(DieselPM_level)
 predictor <- calenviroscreen %>% pull(Traffic_level)
-oddsratio(predictor, response)
 ```
 
-See this [case study](https://www.opencasestudies.org/ocs-bp-vaping-case-study/#Logistic_regression_%E2%80%9Cby_hand%E2%80%9D_and_by_model) for more information.
+## Odds ratios {.smaller}
+
+Use `oddsratio(x, y)` from the `epitools()` package to calculate odds ratios.
+
+In this case, we're calculating the odds ratio for whether living in a high traffic area predicts high levels of DieselPM.
+
+```{r}
+oddsratio(predictor, response)
+```
 
 ## Final note
 

modules/Statistics/Statistics.html

@@ -4111,6 +4111,10 @@ <h1 data-config-title><!-- populated from slide_config.json --></h1>
 
 Number of Fisher Scoring iterations: 16</pre>
 
+</article></slide><slide class=""><hgroup><h2>Logistic Regression</h2></hgroup><article  id="logistic-regression-2">
+
+<p>See this <a href='https://www.opencasestudies.org/ocs-bp-vaping-case-study/#Logistic_regression_%E2%80%9Cby_hand%E2%80%9D_and_by_model' title=''>case study</a> for more information.</p>
+
 </article></slide><slide class=""><hgroup><h2>Odds ratios</h2></hgroup><article  id="odds-ratios">
 
 <blockquote>
@@ -4135,8 +4139,15 @@ <h1 data-config-title><!-- populated from slide_config.json --></h1>
         TrafficPctl &lt;= 0.75 ~ 0))
 
 response &lt;- calenviroscreen %&gt;% pull(DieselPM_level)
-predictor &lt;- calenviroscreen %&gt;% pull(Traffic_level)
-oddsratio(predictor, response)</pre>
+predictor &lt;- calenviroscreen %&gt;% pull(Traffic_level)</pre>
+
+</article></slide><slide class=""><hgroup><h2>Odds ratios</h2></hgroup><article  id="odds-ratios-2" class="smaller">
+
+<p>Use <code>oddsratio(x, y)</code> from the <code>epitools()</code> package to calculate odds ratios.</p>
+
+<p>In this case, we’re calculating the odds ratio for whether living in a high traffic area predicts high levels of DieselPM.</p>
+
+<pre class = 'prettyprint lang-r'>oddsratio(predictor, response)</pre>
 
 <pre >$data
          Outcome
@@ -4167,8 +4178,6 @@ <h1 data-config-title><!-- populated from slide_config.json --></h1>
 attr(,&quot;method&quot;)
 [1] &quot;median-unbiased estimate &amp; mid-p exact CI&quot;</pre>
 
-<p>See this <a href='https://www.opencasestudies.org/ocs-bp-vaping-case-study/#Logistic_regression_%E2%80%9Cby_hand%E2%80%9D_and_by_model' title=''>case study</a> for more information.</p>
-
 </article></slide><slide class=""><hgroup><h2>Final note</h2></hgroup><article  id="final-note">
 
 <p>Some final notes:</p>