Week Date Lecture topic Lab topic Reading
1 W Aug. 17 Course introduction
F Aug. 19 Basic concepts in statistics Chp. 1
2 M Aug. 22 Principles of estimation Introduction to R Chp. 2.1-2.3
W Aug. 24 Principles of estimation
F Aug. 26 Intro to statistical modeling Chp. 5.2-5.3
3 M Aug. 29 Intro to statistical modeling Introduction to RMarkdown
W Aug. 31 t-tests and null hypothesis testing Chp. 3.1;
F Sep. 2 t-tests and null hypothesis testing
4 M Sep. 5 Labor Day No lab
W Sep. 7 Randomized ANOVA Chp. 8.1-8.4
F Sep. 9 Randomized ANOVA
5 M Sep. 12 Multiple comparisons R graphics and t-tests Chp. 8.6
W Sep. 14 Statistcal Power
F Sep. 16 Paper discussion: hypothesis testing Johnson 1999
6 M Sep. 19 Contrasts ANOVA* Chp. 7.3
W Sep. 21 Contrasts
F Sep. 23 Buffer
7 M Sep. 26 Transformations Contrasts and power Exam 1 distributed
W Sep. 28 Nonparametrics Chp. 8.5
F Sep. 30 Nonparametrics
8 M Oct. 3 Blocking and blocked designs Transformations and nonparametrics* Chp. 8.2; Exam 1 due
W Oct. 5 Blocking and blocked designs Chp. 10.1-10.2
F Oct. 7 Random and fixed effects
9 M Oct. 10 AB factorial designs Blocking Chp. 9.2
W Oct. 12 AB factorial designs
F Oct. 14 ABC factorial designs Colegrave & Ruxton 2018
10 M Oct. 17 Nested designs Factorial designs
W Oct. 19 Nested designs Chp. 9.1
F Oct. 21 Paper discussion: Pseudoreplication
11 M Oct. 24 Split-plot designs Nested designs* Chp. 11.1-11.3
W Oct. 26 Split-plot designs
F Oct. 28 Fall break
12 M Oct. 31 Repeated measures Split-plot designs Exam 2 distributed
W Nov. 2 Repeated measures
F Nov. 4 Buffer
13 M Nov. 7 Regression review Repeated measures* Chp. 6; Exam 2 due
W Nov. 9 Analysis of covariance Chp. 12
F Nov. 11 Analysis of covariance
14 M Nov. 14 Generalized linear models ANCOVA Chp. 13
W Nov. 16 Logistic regression
F Nov. 18 Poisson regression
15 M Nov. 21 Thanksgiving break Linear models*
W Nov. 23
F Nov. 25
16 M Nov. 28 GLMMs GLMs Exam 3 distributed
W Nov. 30 Model selection
F Dec. 2 Paper discussion: Model selection Tredennick et al. 2021
17 M Dec. 5 Course wrap up No lab

* = Graded assignment