Statistics Fundamentals

Week 1. Summaries and uncertainty, comparing groups, and effect sizes, the foundation for every model in the rest of the course.

Problem Set 1: Statistics Fundamentals — due Monday, October 5, 1:10 PM. Submit on Canvas.

Data: winedata.csv (used in Problem Set 1)

Mon Sep 28 + Wed Sep 30 — A rapid-fire review of intro statistics

Everything from a first statistics course, fast, in one continuous deck: means and expected values, variance and degrees of freedom, distributions and where they come from, checking normality and transforming data, standard errors, the central limit theorem, and confidence intervals; then t-tests and permutation tests, correlation, ANOVA, non-parametric and categorical tests, effect sizes, and multiple testing. Wednesday picks up wherever Monday ends.

Slides (PDF) — both days in one deck (the live-poll links are clickable)
Week1_Stats_Fundamentals.R — one script for the whole week, in the same order as the slides. It has the code behind every figure and number in the lecture (more than we will run in class), and a closing section with the data-frame tools for Problem Set 1. It uses datasets built into R, plus cassava.csv in one section.

Fri Oct 2 — Intro to R crash course, part 2

Optional, for anyone new to R. Script, data, and resources are on the crash course page.

Learning objectives

Summarize data and quantify uncertainty (SD vs. SE, confidence intervals)
Choose and run an appropriate group comparison (t-test, ANOVA, non-parametric alternatives)
Check assumptions and know when they matter
Report and interpret effect sizes, not just p-values

Also useful

Interactive explorers — distribution, t-test, and correlation apps that run in the browser (no R needed)
Packages this week: ggplot2 (all scripts) and GGally (Problem Set 1)

---
title: "Statistics Fundamentals"
---

::: {.week-header}
**Week 1.** Summaries and uncertainty, comparing groups, and effect sizes, the foundation for every model in the rest of the course.
:::

**Problem Set 1: [Statistics Fundamentals](../Module%201/ps-r-stats-intro.html)** — due **Monday, October 5, 1:10 PM**. Submit on Canvas.

**Data:** [winedata.csv](../data/winedata.csv) (used in Problem Set 1)

---

## Mon Sep 28 + Wed Sep 30 — A rapid-fire review of intro statistics

Everything from a first statistics course, fast, in one continuous deck: means and expected values, variance and degrees of freedom, distributions and where they come from, checking normality and transforming data, standard errors, the central limit theorem, and confidence intervals; then t-tests and permutation tests, correlation, ANOVA, non-parametric and categorical tests, effect sizes, and multiple testing. Wednesday picks up wherever Monday ends.

::: {.file-list}
- [Slides (PDF)](../Module%201/Week1_Stats_Fundamentals_slides.pdf) — both days in one deck (the live-poll links are clickable)
- [Week1_Stats_Fundamentals.R](../Module%201/Week1_Stats_Fundamentals.R) — one script for the whole week, in the same order as the slides. It has the code behind every figure and number in the lecture (more than we will run in class), and a closing section with the data-frame tools for Problem Set 1. It uses datasets built into R, plus [cassava.csv](../data/cassava.csv) in one section.
:::

## Fri Oct 2 — Intro to R crash course, part 2

Optional, for anyone new to R. Script, data, and resources are on the **[crash course page](../discussions/r-crash-course-2.html)**.

---

## Learning objectives

- Summarize data and quantify uncertainty (SD vs. SE, confidence intervals)
- Choose and run an appropriate group comparison (t-test, ANOVA, non-parametric alternatives)
- Check assumptions and know when they matter
- Report and interpret effect sizes, not just p-values

## Also useful

- [Interactive explorers](../Module%201/Mod1_interactive.html) — distribution, t-test, and correlation apps that run in the browser (no R needed)
- Packages this week: `ggplot2` (all scripts) and `GGally` (Problem Set 1)