Week 7 analysis using both Neyman-Pearson and Fisher frameworks to test whether central air conditioning and recent construction affect home sale prices. Includes power analysis, sample size calculations, and detailed interpretations of statistical evidence.

Week 6 analysis examining relationships between original and derived variables. Calculates correlation coefficients, builds confidence intervals for population inference, and interprets statistical relationships in practical real estate contexts.

Week 5 analysis examining unclear data encodings, missing value ambiguities, and data quality problems. Investigates what happens when documentation is incomplete and demonstrates methods for detecting inconsistencies and defining outliers.

Week 4 analysis examining how conclusions change based on which random sample we collect. Creates multiple samples at different sizes (25%, 50%, 75%) to understand when findings are robust vs. sample-dependent. Demonstrates the relationship between sample size and reliability.

Week 3 analysis investigating group probabilities and anomaly detection in Ames housing data. Identifies rare building types, quality-neighborhood mismatches, and architectural combinations. Includes probability calculations, rarity classifications, and testable hypotheses.

Comprehensive data analysis of 2,930 Ames, Iowa home sales examining price patterns, neighborhood segmentation, quality-size relationships, and market stability during the 2008 financial crisis. Includes statistical summaries, visualizations, and actionable insights for buyers, sellers, and investors.