ESM 206 Labs - Intro Stats and Data Analysis for Environmental Science (Fall 2018)

Lab Materials

Bren School of Environmental Science and Management, UC Santa Barbara

Course goals:

Note: Repo and course materials are in development; forgive the dust and incompleteness while I work on it.

Week	Lectures	Lab Materials
0	(none)	Step 0: Get R/RStudio instructions
1	Lecture 1: Course intro Lecture 2: Data exploration, probability density, assessing normality	Lab 1: Intro to R/RStudio, R projects, scripts
2	Lecture 3: Central limit theorem, confidence intervals, t-distribution Lecture 4: The hypothesis test system	Lab 2: Basic wrangling and visualization, Rmarkdown intro
3	Lecture 5: What the p-value means (and doesn't), 2-sample t-tests Lecture 6: T-tests continued, assumptions, power	Lab 3: Z-distribution probabilities, t-tests
4	Lecture 7: Effect size, communicating differences, F-test for equal variances Lecture 8: Recap of tests so far, errors & limitations discussion, intro to one-way ANOVA	Lab 4: Exploratory graphs, wrangling, t-test, effect size, power
5	Lecture 9: One-way ANOVA, post-hoc Tukey's HSD, why dynamite plots are a bummer Lecture 10: Rank-based comparisons	Lab 5: Wrangling, power, joins, viz
6	Lecture 11: Ranked-based tests & effect size, chi-square intro Lecture 12: Intro to OLS and linear regression	Lab 6: GitHub intro, wrangling, visualization, ANOVA
7	Lecture 13: Simple linear regression, assumptions, correlation Lecture 14	Lab 7: GitHub continued, chi-square, rank-based tests
8	Lecture 15	Lab 8 (take home): Simple linear regression
9	Lecture 16 Lecture 17	Lab 9: Multiple linear regression
10	Lecture 18 Lecture 19	Lab 10: Heatmaps, gganimate