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CHAPTERS.md — PyStatsV1 Case Study Overview

This document provides a high‑level overview of the statistical “chapters” implemented in PyStatsV1.

Each chapter includes:

  • A simulator (scripts/sim_*)
  • An analyzer (scripts/chXX_*)
  • A Makefile target (make chXX, make chXX-ci)
  • A role in replicating applied statistics workflows

The chapters intentionally follow a “simulator → analysis → artifacts” pattern so students and contributors can learn generalizable structures.

Chapter 13 — Intro to Experimental Psychology & Fitness Statistics

13A: Stroop Task (Within‑Subjects)

Concepts:

  • Paired differences
  • Repeated‑measures design
  • Mixed modeling intro
  • Reaction time data

Files:

  • scripts/sim_stroop.py
  • scripts/ch13_stroop_within.py

Run:

make ch13
make ch13-ci

13B: 2×2 Fitness Study (Mixed Design)

Concepts:

  • Factorial designs
  • Mixed-effects models
  • Confidence intervals & effect sizes
  • Long-format data

Files:

  • scripts/sim_fitness_2x2.py
  • scripts/ch13_fitness_mixed.py

Run:

make ch13
make ch13-ci

Chapter 14 — A/B Tutoring Experiment (Two‑Sample t‑Test)

Concepts:

  • Independent‑samples t-test
  • Random assignment
  • Group means and confidence intervals
  • Power from sample size

Files:

  • scripts/sim_ch14_tutoring.py
  • scripts/ch14_tutoring_ab.py

Run:

make ch14
make ch14-ci

Planned enhancements (v0.17+):

  • 🔍 “Explain Mode” showing calculation steps
  • 📈 Richer summary and visualization

Chapter 15 — Reliability & Psychometrics

Concepts:

  • Cronbach’s Alpha (internal consistency)
  • ICC (test‑retest reliability)
  • Bland–Altman plots
  • Multivariate normal simulation

Files:

  • scripts/sim_ch15_reliability.py
  • scripts/ch15_reliability_analysis.py

Run:

make ch15
make ch15-ci

Planned enhancements:

  • Item–total correlation table
  • Alpha variants (standardized, dropped‑item α)
  • Optional factor‑analytic visualization

Future Chapters (in ROADMAP.md)

  • Epidemiology “Risk Ratio with Strata” simulator + analyzer
  • Power analysis modules
  • Confidence interval bootstrapping
  • Regression diagnostics
  • GLMs for count data
  • Bayesian re‑implementations

If you'd like to contribute to any chapter, see:
👉 CONTRIBUTING.md