00-intro.knit

.title[
# <p>Teaching Bayesian Statistics at the Undergraduate Level</p>
An Overview
]
.subtitle[
## ICOTS 11 workshop
]
.author[
### Mine Dogucu
]
.date[
### 2022-09-11
]

---

## As you get settled in

- Download the whole workshop repo:    
    - [github.com/bayes-rules/icots-2022](https://github.com/bayes-rules/icots-2022)
    - Click on the green "Code" button.
    - Select "download zip".

- Open RStudio (or R).

- Say hello to your neighbor!

---

### Instructor

Mine Dogucu  
University College London

---

### Coauthor

Alicia Johnson  
Macalester College

---

### Coauthor

Miles Ott  
Twitter

---

### TA

Wendy Rummerfield  
California State University East Bay

---

### Bayes Rules! An Introduction to Bayesian Modeling with R

[<svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M172.5 131.1C228.1 75.51 320.5 75.51 376.1 131.1C426.1 181.1 433.5 260.8 392.4 318.3L391.3 319.9C381 334.2 361 337.6 346.7 327.3C332.3 317 328.9 297 339.2 282.7L340.3 281.1C363.2 249 359.6 205.1 331.7 177.2C300.3 145.8 249.2 145.8 217.7 177.2L105.5 289.5C73.99 320.1 73.99 372 105.5 403.5C133.3 431.4 177.3 435 209.3 412.1L210.9 410.1C225.3 400.7 245.3 404 255.5 418.4C265.8 432.8 262.5 452.8 248.1 463.1L246.5 464.2C188.1 505.3 110.2 498.7 60.21 448.8C3.741 392.3 3.741 300.7 60.21 244.3L172.5 131.1zM467.5 380C411 436.5 319.5 436.5 263 380C213 330 206.5 251.2 247.6 193.7L248.7 192.1C258.1 177.8 278.1 174.4 293.3 184.7C307.7 194.1 311.1 214.1 300.8 229.3L299.7 230.9C276.8 262.1 280.4 306.9 308.3 334.8C339.7 366.2 390.8 366.2 422.3 334.8L534.5 222.5C566 191 566 139.1 534.5 108.5C506.7 80.63 462.7 76.99 430.7 99.9L429.1 101C414.7 111.3 394.7 107.1 384.5 93.58C374.2 79.2 377.5 59.21 391.9 48.94L393.5 47.82C451 6.731 529.8 13.25 579.8 63.24C636.3 119.7 636.3 211.3 579.8 267.7L467.5 380z"/></svg>bayesrulesbook.com](https://www.bayesrulesbook.com/)
]

---

## Workshop goals

- Learn a little bit about Bayes (especially if you're new to the topic).

- Think about how you might incorporate Bayes into your teaching.

- Meet other people that are also thinking about Bayes.

- Familiarize yourself with the *Bayes Rules!* book and `bayesrules` package.

---

## Workshop plan

- Introduction and motivation

- Hands on time with code:    
    - Bayesian foundations    
    - Posterior simulation & analysis
    - Regression

- Workshop etiquette: be kind to each other

---

## Our Motivation

- Bayesian methods are becoming more popular

- Computing advances and reevaluation of subjectivity

- Lack of resources for the target audience (advanced undergraduate statistics/ds majors or similarly trained learners)

---

## Pedagogical Approach

- Checking intuition

- Active learning (quizzes and applications)

- Computing & math together

- Compute for a single case, then use built-in functions

---

## Accessibility and Inclusion

- Open-Access

- Visual access (color-palette and alternate text)

- Citations

- Datasets

More on the topic is available on [preprint](https://arxiv.org/abs/2110.06355).

Dogucu, M., Johnson, A. A., & Ott, M. (2021). Framework for Accessible and Inclusive Teaching Materials for Statistics and Data Science Courses. arXiv preprint arXiv:2110.06355.
---

## Resources

- [Undergraduate Bayesian Education Resources](https://undergrad-bayes.netlify.app/)

- [Undergraduate Bayesian Education Network](https://undergrad-bayes.netlify.app/network.html)

- [STATS 115 at UC Irvine](https://www.stats115.com)

- [SDS 390 at Smith College](https://www.dropbox.com/sh/qdmxfapt9vv08xm/AABWnhZNmqOdjnaVyGJ5VLyEa?dl=0)

---

## Unit Overview

1: Bayesian Foundations

<hr>

2: Posterior Simulation & Analysis

<hr>

3: Regression and Classification

<hr>

4: Hierarchical Models

]

]

---

## Quiz yourself!

The probability of flipping Heads on a fair coin is 0.5. How do you interpret this probability?

a. If I flip this coin over and over, roughly 50% will be Heads.

b. Heads and Tails are equally plausible.

c. Both a and b make sense.

<br>

**Score yourself: a = 1, b = 3, c = 2**

---
class: top

## Quiz yourself!

'Candidate A has a 0.9 probability of winning the upcoming election'. How do you interpret this probability?

a. If we observe the election over and over, candidate A will win roughly 90% of the time.

b. Candidate A is much more likely to win than to lose.

c. This calculation is wrong. Candidate A will either win or lose, thus their probability of winning can only be 0 or 1.

<br>

**Score yourself: a = 1, b = 3, c = 1**

---
class: top

## Quiz yourself!

Zuofu claims that he can predict the outcome of a coin flip.   
Kavya claims that she can distinguish natural and artificial sweeteners.
Both Zuofu and Kavya succeeded in 10 out of 10 experiments to test their claims. What do you conclude?

a. You're more confident in Kavya's claim than Zuofu's.

b. The evidence supporting Zuofu's claim is just as strong as the evidence supporting Kavya's claim.

<br>

**Score yourself:** a = 3, b = 1

---
class: top

## Quiz yourself!

If you tested positive for a very rare disease and only got to ask the doctor one question, which would it be?

a. What's the chance that I actually have the disease?

b. If in fact I don't have the disease, what's the chance that I would’ve gotten this positive test result?

<br>

**Score yourself: a = 3, b = 1**

---

## Score yourself!