## course schedule

The (Rmarkdown) source code for these lectures is available at the github repository,
or by replacing the `.slides.html`

suffix with `.Rmd`

in the link below;
the slides are made using reveal.js.
Here are the slides from
Fall 2020/Winter 2021,
Fall 2019/Winter 2020,
and Fall 2018/Winter 2019.

Below:

# Winter 2022

- Week 11 (
*1/4*) -
Survival analysis

- slides: Survival curves
- slides: Cox’s Proportional Hazards
- slides: the Weibull distribution
- slides: Parametric survival analysis
- Homework 11
*(due 1/13)* - Reading: Survival Analysis in R by Emily Zabor

- Week 12 (
*1/11*) -
Categorical data: chi-square for contingency tables, permutation tests.

- slides: The chi-squared test for categorical data
- slides: Permutation testing for categorical data
- slides: Poisson models for categorical data (using brms)
- slides: The chi-squared distribution
- Homework 12
*(group homework, due 1/19)* - Reading: Kruschke, chapter 16 (metric data with one or two groups), and chapter 24 (Poisson, contingency tables)
- Code from in-class demo on permutation testing
- Code from in-class demo on posterior summaries from brms

- Week 13 (
*1/18*) -
Time series models with Stan: mechanistic models

- slides: Writing Stan code
- slides: Time series

- Week 14 (
*1/25*) -
Crossvalidation for model comparison; sparsifying priors and variable selection

- slides: Overfitting and crossvalidation
- slides: Multicollinear example
- slides: Shrinkage (sparsifying priors)
- slides: Overdispersion
- Reading: Kruschke, chapters 17 (one-variable linear models), 18 (multivariate linear models)
- Homework 13
*(group homework, individual writeups due 1/3)*

- Week 15 (
*2/1*) -
Many response variables

- slides: Multivariate responses
- slides: the Multivariate Normal
- Code from in-class demo on correlated Normals
- Group presentations
- Homework 14
*(due 2/11)*

- Week 16 (
*2/8*) -
Data analysis example

- slides: Hurricane Lizards

- Week 17 (
*2/15*) -
Factor analysis, dimensionality reduction, and visualization; clustering; PCA, PCoA, MDS, t-SNE, UMAP

- slides: Dimension reduction and PCA
- Homework 15
*(due 2/22)* - Homework 16
*(due 2/28)*

- Week 18 (
*2/22*) -
Factor analysis contiued; latent factors, deconvolution for mixtures of expression data; nonnegative matrix factorization

- slides: On ordination
- slides: t-SNE
- slides: Deconvolution and NMF
- Homework 17
*(due 3/8)*

- Week 19 (
*3/1*) -
Introduction to spatial statistics and spatial autocorrelation; kernel density estimation.

- slides: Making maps
- slides: Spatial autocorrelation
- slides: Spatial density estimation
- Homework 18
*(due 3/15)*

- Week 20 (
*3/8*) -
Spatial statistics: kernel density estimation and interpolation.

- slides: Random forests
- slides: Review
- Reading:

# Fall 2021

- Week 1 (
*9/29*) -
Overview of data science - description and estimation, uncertainty and simulation, with examples for comparing means and linear regression; smoothing.

- Slides: Reminders of COVID policies
- Slides: Introduction
- Slides: Hypothesis testing and p-values
- Slides: The t distribution
- Slides: The central limit theorem
- In-class coding
- Homework 1
*(due 10/7)* - how to git the slide source
- Reading: Quinn & Keough chapters 1-4

- Week 2 (
*10/6*) -
Analysis of Variance (ANOVA) and experimental design; tidy data; power and false positives

- Slides: Confidence intervals
- Slides: Experimental design
- Slides: Analysis of Variance
- In-class coding
- Homework 2
*(due 10/14)* - Reading: Quinn & Keough chapters 5, 7, 8

- Week 3 (
*10/13*) -
Plotting/visualization, and permutation/bootstrapping

- Slides: Permutation tests
- Slides: Tidy data
- Slides: The bootstrap
- Homework 3
*(due 10/21)* - Reading: Quinn & Keough chapter 9

- Week 4 (
*10/20*) -
Multivariate ANOVA, regression, least-squares likelihood

- Slides: Visualization
- Slides: Multivariate ANOVA
- Slides: Formulas
- Homework 4
*(due 10/28)* - Reading: Quinn & Keough chapter 6

- Week 5 (
*10/27*) -
Model selection; random effects and mixed models - a first look.

- Slides: Linear models and least squares
- Slides: Random effects
- Homework: peer review (see Canvas for assignment)
- Reading: Quinn & Keough chapter 13

- Week 6 (
*11/3*) -
Multiple testing, error rates, and some history.

- Slides: Multiple testing
- Slides: Statistics and eugenics
- Slides: A note on P-value thresholds
- Homework 6
*(due 11/12)* - install Stan

- Week 7 (
*11/10*, no class Thursday) -
Introduction to Bayesian statistics

- Slides: Prior distributions
- Slides: Probability rules
- Slides: The Beta distribution
- Homework 7
*(due 11/18)* - Reading: Kruschke, chapters 1, 2, 4, 5, 6, 7

- Week 8 (
*11/17*) -
Bayesian hierarchical modeling - shrinkage, and sharing power

- Slides: Posterior sampling with MCMC
- Slides: Models with Stan
- Slides: Baseball: Hierarchical logistic models
- Reading: Kruschke, chapters 9, 10

- Week 9 (
*11/24*, no class Thursday) -
Logistic models, and sharing power

- Slides: Generalized Linear Models
- Slides: Intro to brms
- Homework 8
*(due 12/3)* - Reading: Kruschke, chapters 13, 21

- Week 10 (
*12/1*) -
Robust linear models; Generalized Linear Models (GLMs).

- Slides: Cauchy distribution
- Slides: Robust models
- Slides: Poisson distribution
- Slides: Poisson models
- Slides: Summary
- Homework 9
*(due 12/9)* - Reading: Kruschke, chapters 15, 16, 17