Regression.Studio

Interactive visualizations, R programming resources, and references designed to accompany the methods covered in HPM 543. Materials are organized by type and can be used independently or alongside course sessions.

R Resources

R & RStudio Quickstart Guide

Installation, packages, libraries, file types, and getting started with R

Quick Reference

Quick-reference tables for base R, dplyr, ggplot2, tidyverse, and external PDF cheat sheets

Regression in R: A Reference

Common regression commands, model specification, and output interpretation

Mixtape Syntax Translations

Translations of applied examples from Cunningham's The Mixtape from Tidyverse to Base R and data.table syntax

Visualizations: Regression Fundamentals

Interactive

OLS: Single Variable

Minimizing the sum of squared errors with one predictor

Interactive

OLS: Multiple Variables

Extending OLS to multiple predictors

Interactive

3D Regression Plane

Visualizing the regression surface with two predictors

Interactive

SSE vs SAE

Comparing sum of squared errors and sum of absolute errors

Interactive

Least Squares Animated

Step-by-step animation of the OLS minimization process

Interactive

Indicator Variables and Interactions

How categorical variables enter the regression model

Interactive

Simple Group Indicator

How a 0/1 predictor shifts the regression line to compare two groups

Interactive

Linear Model Decomposition

Decompose fitted values into predictor components; builds intuition for partial regression and Frisch-Waugh-Lovell

Visualizations: Diagnostics and Extensions

Interactive

Collinearity Bands

How multicollinearity affects coefficient estimation and confidence bands

Interactive

Controlling for a Variable

Interactive

Logistic Regression: Full MLE

Negative log-likelihood surface and maximum likelihood estimation

Visualizations: Causal Inference

Interactive

Regression Discontinuity

Bandwidth selection, polynomial fit, and local ATE estimation at the cutoff

Interactive

Interrupted Time Series

Pre/post treatment effect estimation with trend adjustment

Interactive

Matching Strategies

Comparing propensity score, caliper, and coarsened exact matching

Interactive

ATE vs ATT

Average treatment effect vs treatment effect on the treated; selection bias and overlap

Game

Confounding Game

Identify backdoor paths and valid adjustment sets in directed acyclic graphs

R Syntax Translations

Chapter-by-chapter R translations for the statistical methods and examples in both course texts. Each guide maps concepts to working R code, with tabs for multiple dialects where available.