🎲 Chance Variability & Box Models – DATA1001 USYD Week 7 Notes

Description

Description: These Week 7 notes from DATA1001 (Intro to Data Science) at the University of Sydney give a full breakdown of chance processes, box models, and the central limit theorem, with relatable examples and worked problems. ✅ Topics Covered: Law of Averages & Chance Variability Gambler’s Fallacy vs True Randomness Law of Large Numbers (Kerrich’s Coin Toss Experiment) Box Models Explained: structure, mean, SD, EV, SE Applied Examples: Coin toss Die games Roulette Multiple choice quiz scoring Keno simulation Standard Error: how to calculate and interpret it Modeling with Normal Distributions Central Limit Theorem (CLT) – visualised and explained Continuity Correction for discrete-to-normal approximations Probability, simulation, and data histograms 📘 What’s Inside: Real-world chance game breakdowns Step-by-step formulas: EV, SE, SD Visual intuition on sampling distributions Great for mastering SE, normal approximation, and box models 📝 A must-have for any student aiming to revise or solidify their understanding of randomness, probability models, and inferential frameworks.