Course content (BIOL 678)


Please use the links below to access the content for each topic (week). Please review the rubric for submitting programming assignments through Blackboard each week prior to submission.

The lecture tutorials provided below are linked directly to the online version of The Worst Stats Text eveR, the text book used for the class. The links below will not open in a new tab unless you tell them to. PDF and eBook formats are also available (free).

Introduction to programming in R


Lecture tutorials

Chapter 1: Introduction to programming in R
Chapter 2: Data Structures

Homework exercises

Introduction to R

Data manipulation and visualization


Lecture tutorials

Chapter 3: Working with data
Chapter 4: Plotting and graphics

Homework exercises

Data manipulation and visualization

Probability and sampling distributions


Lecture tutorials

Chapter 5: Sampling distributions

Homework exercises

Sampling distributions

Introduction to inferential statistics


Lecture tutorials

Chapter 6: Intro to inferential

Homework exercises

Hypothesis testing

Linear models


Lecture tutorials

Chapter 7: Linear models
Chapter 8: General linear models

Homework exercises

Linear models

Diagnostics and effect sizes


Lecture tutorials

Chapter 9: Assumptions of linear models
Chapter 10: Communicating effects

Homework exercises

Diagnostics and effects

Model selection


Lecture tutorials

Chapter 11: Model selection

Homework exercises

Model selection

Introduction to Bayesian Inference


Lecture tutorials

Chapter 16: Intro to Bayes

Homework exercises

No homework for this unit.

GLM: Logistic regression


Lecture tutorials

Chapter 12: Introduction to GLM and Logistic regression

Chapter 17: Bayesian logistic regression regression

Homework exercises

Logistic regression

GLM: Count models


Lecture tutorials

Chapter 13: Count models
Chapter 18: Bayesian count models

Homework exercises

Count models

Generalized linear mixed models


Lecture tutorials (MLE)

Chapter 14: Linear mixed models
Chapter 15: Generalized linear mixed models

Lecture tutorials (Bayesian)

Chapter 19: Bayesian hierarchical linear models
Chapter 20: Bayesian hierarchical GLMs

Homework exercises

Hierarchical models




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