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recordings.qmd
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recordings.qmd
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---
title: "Recordings"
---
# Getting started
```{r}
#| echo: false
library(shiny)
tags$div(class = "boxChapter",
tags$div(class = "boxVid", tags$iframe(src="https://www.youtube.com/embed/iIWdf7_AAMM", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Access and submit assessments using GitHub and RMarkdown"))
)
```
# Week 1. Directed Acyclic Graphs (DAGs)
```{r}
#| echo: false
tags$div(class = "boxChapter",
tags$div(class = "boxVid", tags$iframe(src="https://www.youtube.com/embed/lhkmc-dXC7M", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Overview")),
tags$div(class = "boxVid", tags$iframe(src="https://www.youtube.com/embed//DhHBMvpYeec", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Representing common causal structures with DAGs"))
)
```
<aside>
<hr class=myHr>
<div class=myAside>
<p>
To watch the video in full-screen, first click play then choose Full screen.
</p>
</div>
</aside>
# Week 2. Matching for causal inference from observational data
```{r}
#| echo: false
tags$div(class = "boxChapter",
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/cvb17LGNKQ4", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Overview")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/rgXD4MnPDgc", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Identifiability assumptions")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/bOA954e4HcU", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Exact matching and coarsened exact matching")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/q2RE86gZ2Yg", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Distance-based matching")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/ABqX6h6I9d4", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Mahalanobis distance")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/8dRpXafdcF4", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Propensity score distance")),
)
```
# Week 3. Multilevel modelling I (Introduction)
```{r}
#| echo: false
tags$div(class = "boxChapter",
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/OzGgbrGn7ls", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Overview")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/eBItxNkZCLs", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Introducing multilevel data and multilevel models")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/mEcXhYNkJ38", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Fixed parameters or random variables")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/v5QKE7HoGuc", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Residuals and partitioning the variance")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/BIroLtHSKGk", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Correlated random effects")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/Yd0lH6O_X4I", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Multilevel models in R")),
)
```
# Week 4. Multilevel Modelling II (Beyond the basics)
```{r}
#| echo: false
tags$div(class = "boxChapter",
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/8SnPFQK0hf0", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Overview")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/-Pdzm-1E75E", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Model building and comparison")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/QrZ9U9tpb7E", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Model predictions")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/U3inYIjaBNo", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Modelling binary outcomes")),
)
```
# Week 7. Time Series Analysis
```{r}
#| echo: false
tags$div(class = "boxChapter",
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/W6Vfa3wEfjU", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Overview")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/eMp3s_X2uOc", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("What are time series data?")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/YZ9LrP5O9wc", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Visualising time series data")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/UEWbiPB81Fk", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Characterisitcs of time series data")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/9wQJTOOS6lQ", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Statistical properties of time series data")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/DlHmaTqvCxQ", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("ARIMA models"))
)
```
# Week 8. Interrupted Time Series Analysis
```{r}
#| echo: false
tags$div(class = "boxChapter",
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/hEKYDvGzOFw", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Overview")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/4o_DDdhPz7Y", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("What is interrupted time series analysis?")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/vbZU8tq-IUw", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Segmented regression")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/soL_RXNAIqE", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Model choice"))
)
```
# Week 9. Presenting and summarising model results
```{r}
#| echo: false
tags$div(class = "boxChapter",
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/T4X8i4H_Rpo", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Overview")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/gbokhfMdfE8", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Interpreting and Presenting Statistical Results by Professor Gary King")),
)
```
# Week 9. Missing Data and Multiple Imputation
```{r}
#| echo: false
tags$div(class = "boxChapter",
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/VSEqd6MuKP0", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Iterative Chained Equations (ICE)")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/H1kAcipjUH4", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("Advantages of the ICE algorithm")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/XGy-OYqvC-0", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("How the ICE algorithm works")),
tags$div(class = "boxVid", tags$iframe(src="https://youtube.com/embed/QxLMUv7YKU0", title="YouTube video player", frameborder="0", allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen")),
tags$div(class = "boxText", tags$p("How many cycles for ICE?"))
)
```