/esm-206-2018

Bren School ESM 206 (Intro Data Analysis & Stats) materials

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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:

  • Intro to data analysis in R/RStudio
  • Data wrangling with the tidyverse
  • Exploratory data analysis
  • Basic summary statistics & hypothesis testing
  • Data visualization
  • Reproducible workflows with RProjects, Rmarkdown, and GitHub

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

Get R, RStudio and the tidyverse package: step-by-step instructions

Get and configure GitHub: step-by-step instructions

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