Week 10 analysis using logistic regression to predict whether homes achieve premium quality ratings (8-10 out of 10). Models probability using living area, year built, garage size, and central air. Interprets coefficients as odds ratios, constructs confidence intervals using standard errors, and evaluates model performance with confusion matrix and accuracy metrics.

Week 9 revised analysis building multiple regression model with living area, quality rating, and central air variables. Includes comprehensive diagnostics using the 5 plots from class: residuals vs fitted, residuals vs X values, correlation heatmap, residual histogram, and Q-Q plot. Evaluates linearity, normality, homoscedasticity, and multicollinearity with severity assessments and confidence levels for each assumption.

Week 8 analysis using ANOVA to test house style effects on price and simple linear regression to model the relationship between living area and sale price. Includes assumption checking, effect sizes, diagnostic plots, and practical recommendations for buyers and sellers.

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.