Lecture information


Date	Topic(s)	Slides	Source	Background info
30 March	Course overview	html	Rmd	R software RStudio Intro to R Markdown
1 April	Ecological data & distributions Detections Counts Survival	html	Rmd	Understanding data Statistical distributions Distribution relationships
3 April	Linear models Non-linear approximations Regression, ANOVA, ANCOVA	html	Rmd	Farraway (2015) Chap 2
6 April	Linear models Models in matrix form Least squares Identifiability	html	Rmd	Farraway (2015) Chap 2
8 April	Inference Variance partitioning F-tests for nested models Confidence intervals	html	Rmd	Farraway (2015) Chap 3 Farraway (2015) Chap 4 Wasserstein et al. (2019)
10 April	Inference Variance partitioning F-tests for nested models Confidence intervals	html	Rmd	Farraway (2015) Chap 3 Farraway (2015) Chap 4 Wasserstein et al. (2019)
13 April	Model diagnostics Assumptions about errors Leverage Outliers	html	Rmd	Farraway (2015) Chap 6
15 April	Problems with model errors Generalized least squares Weighted least squares Robust regression	html	Rmd	Farraway (2015) Chap 8
17 April	Data transformations Box-Cox Powers/roots Logarithms	html	Rmd	Farraway (2015) Chap 9 O’Hara & Kotze (2010) Xiao et al. (2011)
20 April	Design matrices Models in matrix form Regression ANOVA / ANCOVA	html	Rmd	Venables (2018)
22 April	Design matrices Models in matrix form Regression ANOVA / ANCOVA	html	Rmd	Venables (2018)
24 April	Maximum likelihood estimation Relationship to probability Estimation Characteristics of MLE	html	Rmd	Faraway (2016) Appendix A
27 April	Model selection Bias-Variance trade-offs In-sample selection AIC	html	Rmd	Burnham & Anderson (2002) Hobbs & Hilborn (2006)
29 April	Model selection Out-of-sample selection Cross-validation Multi-model inference	html	Rmd	Aho et al (2014) Banner & Higgs (2017) Burnham & Anderson (2002) Cade (2015) Hooten & Hobbs (2015) Link & Barber (2006)
1 May	Review session All topics to date
4 May	Mixed effects models Fixed vs Random effects Shrinkage Costs/benefits	html	Rmd	Freeman’s visualization Harrison et al (2018) Zuur et al (2009) Chap 5
6 May	Mixed effects models Inference Diagnostics Model selection	html	Rmd	Gurka (2006) Harrison et al (2018)
8 May	Guest lecture: Dr. Sarah Converse Research questions, hypotheses & predictions			Chamberlin (1890) Garton et al (2020)
11 May	Introduction to GLMs Data distributions Link functions Linear predictors	html	Rmd	Nelder & Wedderburn (1972) Faraway (2016) Chap 8
13 May	Modeling binary data Logistic regression Model selection Diagnostics Project plan due	html	Rmd	Faraway (2016) Chap 2
15 May	Overdispersion in binary data Variance inflation Beta-binomial modes Quasi-likelihood	html	Rmd	Faraway (2016) Chap 2 & 3
18 May	Modeling count data Poisson regression Leverage and influence Diagnostics	html	Rmd	Faraway (2016) Chap 5 St-Pierre et al. (2017)
20 May	Overdispersion in count data Variance inflation Quasi-likelihood Negative-binomial distribution	html	Rmd	Faraway (2016) Chap 5 Ver Hoef & Boveng (2007)
22 May	Zero-truncated & zero-inflated models Zero truncation Zero inflation Hurdle models Mixture models	html	Rmd	Faraway (2016) Chap 5 Zuur et al (2009) Chap 11 Blasco-Moreno (2019)
25 May	Memorial Day - No class
27 May	Working with GLMMs Computing likelihoods	html	Rmd	Faraway (2016) Chap 13 Zuur et al (2009) Chap 13 Bolker et al (2009)
29 May	Working with GLMMs Diagnostics Goodness-of-fit	html	Rmd	Faraway (2016) Chap 13 Zuur et al (2009) Chap 13 Bolker et al (2009)
1 June	Intro to GAMs Smooths	html	Rmd	Zuur et al (2009) Chap 3 Pedersen et al (2019)
3 June	Working with GAMs Fitting models	html	Rmd	Zuur et al (2009) Chap 3 Pedersen et al (2019)
5 June	Course synthesis What did we learn? Where do we go from here?	html	Rmd