diff --git a/SSR/.quarto/_freeze/OverviewSSR/execute-results/html.json b/SSR/.quarto/_freeze/OverviewSSR/execute-results/html.json
index 13f697d..2870143 100644
--- a/SSR/.quarto/_freeze/OverviewSSR/execute-results/html.json
+++ b/SSR/.quarto/_freeze/OverviewSSR/execute-results/html.json
@@ -1,7 +1,7 @@
{
"hash": "cd1a57e67329a9d3fa1408161c978444",
"result": {
- "markdown": "---\ntitle: \"SSR - Overview\"\nauthor: \"Johnny van Doorn\"\ndate: today\nformat:\n html:\n toc: true\n toc-location: left\n---\n\n\nThis page will be continuously updated to feature extra materials, such as applets, data sets, and additional explanations.\n\n## Slides\n\n\n\n\n\n\n\n\n\nThese slides were originally created by SH and tweaked by JvD.\n\n::: {.cell}\n::: {.cell-output-display}\n`````{=html}\n
\n\n`````\n:::\n:::\n\n\n## Book\nAn online version of Discovering Statistics Using JASP is available [here](https://amsuni-my.sharepoint.com/:b:/g/personal/j_b_vandoorn_uva_nl/ESR4n5L9HB5KsKByVbDlsj0BoZLpYD2aWwJstikmEGU25g?e=dKODJR). Individual chapters available [here](https://amsuni-my.sharepoint.com/:f:/g/personal/j_b_vandoorn_uva_nl/EoyhXwuTGZtGvJaobtAr83kBCA-BWxgKN6-kPvKIhKxaEg?e=k0eNPP). The supplementary materials (data files, Oliver Twisted, etc.) for the book are available [here](https://johnnydoorn.github.io/discoverjasp/pages/data.html).\n\n## Applets\n\n::: {.cell}\n::: {.cell-output-display}\n`````{=html}\n
\n\n`````\n:::\n:::\n\n\n\n## JASP Gifs\n\n\n\n\n\n\n\n\n\nTo accommodate the use of JASP in SSR, below are various instructional `.gifs`.\n\n::: {.cell}\n::: {.cell-output-display}\n`````{=html}\n
\n\n`````\n:::\n:::\n",
+ "markdown": "---\ntitle: \"SSR - Overview\"\nauthor: \"Johnny van Doorn\"\ndate: today\nformat:\n html:\n toc: true\n toc-location: left\n---\n\n\nThis page will be continuously updated to feature extra materials, such as applets, data sets, and additional explanations.\n\n## Slides\n\n\n\n\n\n\n\n\n\nThese slides were originally created by SH and tweaked by JvD.\n\n::: {.cell}\n::: {.cell-output-display}\n`````{=html}\n
\n\n`````\n:::\n:::\n\n\n## Book\nAn online version of Discovering Statistics Using JASP is available [here](https://amsuni-my.sharepoint.com/:b:/g/personal/j_b_vandoorn_uva_nl/ESR4n5L9HB5KsKByVbDlsj0BoZLpYD2aWwJstikmEGU25g?e=dKODJR). Individual chapters available [here](https://amsuni-my.sharepoint.com/:f:/g/personal/j_b_vandoorn_uva_nl/EoyhXwuTGZtGvJaobtAr83kBCA-BWxgKN6-kPvKIhKxaEg?e=k0eNPP). The supplementary materials (data files, Oliver Twisted, etc.) for the book are available [here](https://johnnydoorn.github.io/discoverjasp/pages/data.html).\n\n## Applets\n\n::: {.cell}\n::: {.cell-output-display}\n`````{=html}\n
\n\n`````\n:::\n:::\n\n\n\n## JASP Gifs\n\n\n\n\n\n\n\n\n\nTo accommodate the use of JASP in SSR, below are various instructional `.gifs`.\n\n::: {.cell}\n::: {.cell-output-display}\n`````{=html}\n
Power becomes quite abstract when we increase the complexity (i.e., number of predictors) of our models. We can make an F-distribution that symbolizes the alternative distribution by shifting the distribution more to the right (although the interpretability becomes pretty murky..) ::: {.cell} ::: {.cell-output-display} ::: :::
+
Power becomes quite abstract when we increase the complexity (i.e., number of predictors) of our models. We can make an F-distribution that symbolizes the alternative distribution by shifting the distribution more to the right (although the interpretability becomes pretty murky..) ::: {.cell} ::: {.cell-output-display} ::: :::
Adjusted/marginal means
@@ -749,30 +764,30 @@
Adjusted/marginal means
Adjusted/marginal means
-
# Add dummy variables
-data$dummy.1<-ifelse(data$nationality =="German", 1, 0)
-data$dummy.2<-ifelse(data$nationality =="Belgian", 1, 0)
-
-# b coefficients
-b.cov <- fit$coefficients["openness"]; b.int = fit$coefficients["(Intercept)"]
-b.2<- fit$coefficients["nationalityGerman"]; b.3= fit$coefficients["nationalityBelgian"]
-
-# Adjustment factor for the means of the independent variable
-data$mean.adj <-with(data, b.int + b.cov *mean(openness) + b.2* dummy.1+ b.3* dummy.2)
-
-aggregate(mean.adj ~ nationality, data, mean)
+
# Add dummy variables
+data$dummy.1<-ifelse(data$nationality =="German", 1, 0)
+data$dummy.2<-ifelse(data$nationality =="Belgian", 1, 0)
+
+# b coefficients
+b.cov <- fit$coefficients["openness"]; b.int = fit$coefficients["(Intercept)"]
+b.2<- fit$coefficients["nationalityGerman"]; b.3= fit$coefficients["nationalityBelgian"]
+
+# Adjustment factor for the means of the independent variable
+data$mean.adj <-with(data, b.int + b.cov *mean(openness) + b.2* dummy.1+ b.3* dummy.2)
+
+aggregate(mean.adj ~ nationality, data, mean)
nationality mean.adj
-1 Dutch 39.19740
-2 German 41.96675
-3 Belgian 43.37883
+1 Dutch 38.86557
+2 German 41.96471
+3 Belgian 42.63219
-
aggregate(outcome ~ nationality, data, mean)
+
aggregate(extraversion ~ nationality, data, mean)
-
nationality outcome
-1 Dutch 38.67000
-2 German 42.16625
-3 Belgian 43.79800
+
nationality extraversion
+1 Dutch 40.87000
+2 German 42.03889
+3 Belgian 41.39143
::: :::
