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FANR6750
2023.1.0
Syllabus
Schedule
Lectures
Lecture 0: Course introduction
Lecture 1: Introduction to statistics
Lecture 2: Introduction to linear models
Lecture 3: Principles of inference
Lecture 4: Linear models part 1: categorical predictor w/ 2 levels
Lecture 5: Null hypothesis significance testings
Lecture 6: Statistical power
Lecture 7: Linear models part 2: categorical predictor > 2 levels
Lecture 8: Multiple Comparisons
Lecture 9: Multiple Regression
Lecture 10: Interactions
Lecture 11: Evaluating assumptions
Lecture 12: Model selection
Lecture 13: Random effects
Lecture 14: Nested designs
Lecture 15: Split-plot designs
Lecture 16: Repeated measures
Lecture 17: Generalized linear models
Lecture 18: Logistic regression
Lecture 19: Poisson regression
Lecture 20: Zero-inflated regression
Labs
Lab 1: Introduction to R
Lab 2: Introduction to projects and RMarkdown
Lab 3: t-tests
Lab 4: ANOVA
Lab 5: Multiple Regression
Lab 6: Interactions
Lab 7: Evaluating assumptions
Lab 8: Model selection
Lab 9: Nested designs
Lab 10: Split-plot designs
Lab 11: Repeated measures
Lab 12: Logistic regression
Lab 13: Poisson regression
References
Assignment instructions
Projects and directories
R Markdown reference
Creating publication-quality graphics
Articles
All vignettes
Introduction to R Markdown
Checklist and tips for publication-quality graphics in R
Homework instructions
Lab 1: Introduction to R
Lab 2: RMarkdown and RStudio projects
Lab 3: Linear models with one categorical predictor-- t-tests
Lab 4: Completely randomized ANOVA
Lab 5: Multiple Regression
Lab 6: Interactions
Lab 7: Evaluating assumptions of Linear Models
Lab 8: Model Selection
Lab 9: Nested ANOVA
Lab 10: Split plot designs
Lab 11: Repeated measures
Lab 12: Generalized linear models: Logistic Regression
Lab 13: Generalized linear models: Poisson Regression
Improving your workflow through projects
Schedule (subject to change)
Syllabus