Recorded lectures (YouTube)

Lecture notes

  1. The bootstrap method (PDF) - Data: pruche.csv

  2. Randomization tests (PDF) - Data: sphagnum_cover.csv, environment.csv

  3. Maximum likelihood (PDF) - Data: galapagos.csv

  4. Robust regression (PDF)

  5. Generalized linear mixed models (PDF) - Data: rikz.csv

  6. Generalized linear mixed models 2 (PDF) - Data: acer_transplant.csv

  7. Generalized additive models (PDF)

  8. Introduction to Bayesian analysis (PDF) - Data: galapagos.csv

  9. Hierarchical Bayesian models (PDF) - Data: rikz.csv

  10. Hierarchical Bayesian models (supplement) (PDF) - Data: rikz.csv

  11. Time series (PDF) - Data: sea_ice.txt, dendro_wa082.csv

  12. Spatial data (PDF) - Data: semis_xy.csv

Exercises

  1. The bootstrap method (solutions) (PDF) - Data: sphagnum_cover.csv

  2. Randomization tests (solutions) (PDF) - Données: sablefish.csv

Graded lab: Randomization tests and bootstrap (PDF) - Data: portal_surveys.csv, portal_plots.csv

  1. Maximum likelihood (solutions) (PDF) - Data: thermal_range.csv

  2. Graded lab: Robust regression (PDF)

  3. Generalized linear mixed models (solutions) (PDF) - Data: portal_surveys.csv, portal_species.csv, portal_plots.csv

  4. Graded lab: Generalized linear mixed models 2 (PDF) - Data: aiv_ducks.csv

  5. Generalized additive models (solutions) (PDF) - Data: dendro_wa082.csv

Graded lab: Generalized additive models (PDF) - Data: portal_ot.csv

  1. Introduction to Bayesian analysis (solutions) (PDF) - Data: thermal_range.csv

  2. Graded lab: Hierarchical Bayesian models (PDF)

  3. Supplement: Stan coding example (PDF) - Data: rikz.csv

  4. Time series (solutions) (PDF) - Data: oak_seeds.csv, oak_weather.csv

Graded lab: Time series (PDF) - Data: EOBS_fluxnet2.csv, EOBS_fluxnet_inmet2.txt

  1. Spatial data (solutions) (PDF) - Data: bryo_belg.csv