Labs
Guided practice in data analysis for public policy
The labs connect statistical reasoning, R programming, and interpretation. We will complete most of each lab together on Wednesday. Friday sections revisit the central ideas, troubleshoot code, and support the On Your Own work.
Each lab has two parts: a detailed online guide and a student lab template for students to complete and render.
Lab 0: Getting started with R, RStudio, and Quarto
Set up the software workflow, use R as a calculator, create objects, and render a first reproducible report.
Lab 1: Introduction to data analysis in R
Use historical birth records to learn the basic analysis cycle: inspect, transform, visualize, calculate, and interpret.
Lab 2: Describing and comparing data in R
Use CDC survey data to connect distributions, summary statistics, group comparisons, and appropriate visualizations.
Lab 3: Probability, independence, and simulation
Use shot-by-shot basketball data to practice conditional probability, independence, simulation, and streaks.
Lab 4: Normal and binomial models with Ames housing
Use Ames housing data to compare empirical probabilities with a fitted normal model, then build and interpret a binomial model from the observed data.