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slides-overview.qmd
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---
title: "Overview"
subtitle: "HDAT9700 Statistical Modelling II"
author: Mark Hanly
format:
revealjs:
chalkboard: true
preview-links: auto
logo: images/Landscape__1.Col_Pos_CBDRH.png
footer: "© UNSW 2023"
slide-number: c/t
theme: ["theme-hdat9700.scss"]
title-slide-attributes:
data-background-image: images/galaxy.jpeg
data-background-size: contain
---
## {background-image="https://media0.giphy.com/media/z3HLBSikmgCcIdjnCU/giphy.gif?cid=ecf05e47pvngwswmk3evn73jbr8k4a30x6rp0qvg9zxk7lb1&rid=giphy.gif&ct=g" background-size="contain"}
::: {.aside}
<a style="color: grey;" href="https://giphy.com/gifs/preguica-babybluecat-cocofofo-z3HLBSikmgCcIdjnCU">via GIPHY</a>
:::
## {background-image=images/slides/overview/cairns.jpg}
## {background-image=images/slides/overview/kayak.jpg}
## {background-image=images/slides/overview/leatherwork.jpg background-size=contain}
## {background-image=images/slides/overview/molly.png background-size=contain}
<!-- {.absolute top="50" left="0" height="300"} -->
<!-- {.absolute top="50" left="600" width="300"} -->
<!-- {.absolute top="340" left="650" height="350"} -->
<!-- {.absolute top="390" left="100"} -->
## People
::: columns
::: {.column width="33%"}
{fig-align="left"}
- Course convenor
- Lecturer (Chapters 1-8)
:::
::: {.column width="33%"}
{fig-align="left"}
- Guest lecture (Chapter 9)
:::
::: {.column width="33%"}
{fig-align="left"}
- Guest lecture (Chapter 10)
:::
:::
## Course content
::: {style="font-size: 0.85em;"}
| Chapter | Topic | Lecturer |
|---------|--------------------------------------------|----------|
| 1 | Directed Acyclic Graphs (DAGs) | Mark |
| 2 | Causal inference from observational data | Mark |
| 3 | Multilevel Modelling I (Introduction) | Mark |
| 4 | Multilevel Modelling I (Beyond the basics) | Mark |
| 5 | Multilevel Modelling I (Repeated measures) | Mark |
| 6 | Reading week | |
| 7 | Time series analysis | Mark |
| 8 | Interrupted time series analysis | Mark |
| 9 | Missing data and multiple imputation | Jason |
| 10 | Presenting and summarising model results | Shawon |
:::
## Assessments
| Assessment | Topic | Week | Weight |
|------------|----------------------|-------------|------------------|
| 1A | Causal inference | 1 - 2 | $\frac{50\%}{3}$ |
| 1B | Multilevel modelling | 3 - 5 | $\frac{50\%}{3}$ |
| 1C | Time series analysis | 7 - 8 | $\frac{50\%}{3}$ |
| 2 | Final report | Your choice | 50% |
## Software and platforms
<br>
### Microsoft Teams
::: columns
::: {.column width="50%"}
.svg)
:::
::: {.column width="50%"}
- Announcements
- Questions
- Discussion
- Online tutorials
:::
:::
## Software and platforms
<br>
### RStudio
::: columns
::: {.column width="50%"}
:::
::: {.column width="50%"}
- `MatchIt` for propensity score matching
- `lme4` for multilevel modelling
- `forecast` for time series analysis
:::
:::
## Software and platforms
<br>
### RMarkdown
::: columns
::: {.column width="50%"}
:::
::: {.column width="50%"}
- Getting started? Check out the [`RMarkdown` cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf)
:::
:::
## Software and platforms
<br>
### GitHub
::: columns
::: {.column width="50%"}
:::
::: {.column width="50%"}
- Getting started? Check out [Happy Git and GitHub for the useR](https://happygitwithr.com/index.html)
:::
:::
<br>
## Course website
Visit [hdat9700.cbdrh.med.unsw.edu.au](https://hdat9700.cbdrh.med.unsw.edu.au/)
* Course outline
* Weekly schedule
* Content links
* Assessment and feedback download links
## Workflow
UNSW expectation is [**10-15 hours of study and learning activities per week**]{style="color: #18bc9c"} for a six-units-of-credit course.
You can use this time to
- Read core chapter readings
- Work through interactive learnr tutorials
- Attend weekly tutorial sessions
- Ask questions and join discussions on Teams
## Contact and questions
- Teams is best! But please \@ me for visibility
- Email is ok but I may repost your question to Teams (anonymously) if the response is of general interest
- Weekly office hours (flexible hours - message to arrange)