| Week | Date | Lecture topic | Lab Topic | |
|---|---|---|---|---|
| 1 | M | Aug. 17 | Course introduction | Introduction to R |
| W | Aug. 19 | Introduction to statistics | ||
| F | Aug. 21 | Introduction to linear models | ||
| 2 | M | Aug. 24 | Introduction to linear models | Introduction to RMarkdown |
| W | Aug. 26 | Principles of statistical inference | ||
| F | Aug. 28 | Principles of statistical inference | ||
| 3 | M | Aug. 31 | Linear models for simple categorical predictors (aka t-test) | Sampling error* |
| W | Sep. 2 | Linear models for simple categorical predictors (aka t-test) | ||
| F | Sep. 4 | Troubleshooting R workshop | ||
| 4 | M | Sep. 7 | Labor Day | No lab |
| W | Sep. 9 | Null hypothesis testing | ||
| F | Sep. 11 | Null hypothesis testing | ||
| 5 | M | Sep. 14 | Evaluating assumptions | t-tests/NHST |
| W | Sep. 16 | Buffer/exam review | ||
| F | Sep. 18 | Exam 1 | ||
| 6 | M | Sep. 21 | Principles of causal inference | Evaluating Assumptions* |
| W | Sep. 23 | Principles of causal inference | ||
| F | Sep. 25 | Principles of experimental design | ||
| 7 | M | Sep. 28 | Principles of experimental design | Experimental Design |
| W | Sep. 30 | Linear models for simple categorical predictors (aka t-test) | ||
| F | Oct. 2 | Linear models for simple categorical predictors (aka t-test) | ||
| 8 | M | Oct. 5 | Multiple comparisons | ANOVA* |
| W | Oct. 7 | Power | ||
| F | Oct. 9 | Special topic: p-hacking | ||
| 9 | M | Oct. 12 | Multiple regression | Multiple Comparisons* |
| W | Oct. 14 | Multiple regression | ||
| F | Oct. 16 | Interactions | ||
| 10 | M | Oct. 19 | Interactions | Multiple Regression |
| W | Oct. 21 | Buffer/exam review | ||
| F | Oct. 23 | Exam 2 | ||
| 11 | M | Oct. 26 | Causal Inference for Observational Studies | Interactions* |
| W | Oct. 28 | Causal Inference for Observational Studies | ||
| F | Oct. 30 | Fall Break | ||
| 12 | M | Nov. 2 | Model Selection | Causal Inference* |
| W | Nov. 4 | Psuedoreplication Part 1: Random effects | ||
| F | Nov. 6 | Psuedoreplication Part 1: Random effects | ||
| 13 | M | Nov. 9 | Psuedoreplication Part 2: Nested designs | Model Selection* |
| W | Nov. 11 | Psuedoreplication Part 3: Repeated Measures | ||
| F | Nov. 13 | Special topic: AI | ||
| 14 | M | Nov. 16 | Generalized linear models | Nested Designs* |
| W | Nov. 18 | GLM: Logistic regression | ||
| F | Nov. 20 | GLM: Poisson regression | ||
| 15 | M | Nov. 23 | Thanksgiving break | No lab |
| W | Nov. 25 | Thanksgiving break | ||
| F | Nov. 27 | Thanksgiving break | ||
| 16 | M | Nov. 30 | Exam 3 | No lab |
Schedule (subject to change)
FANR 6750: Experimental Methods in Forestry and Natural Resources Research
Fall 2026
* = Graded assignment