Syllabus.
Week 1. Stem-and-leaf, 5-number summary, boxplot, parallel boxplots, examples
Week 2. EDA vs. CDA vs. DM, magical thinking, scatter plots, pairs(), bagplot(), spin()
Week 3. Summaries of location, spread vs. level plot, empirical Q-Q plot, smoothing scatter plots, smoothing types
Week 4. "The future of data analysis", linear fitting, OLS, WLS, NLS, multiple OLS, robust/resistant fitting of straight line
Week 5. Optimization methods, the psi function, residual analysis, fitting by stages, the x-values
Week 6. Wavelets, NLS, robust/resistant variants, smoothing/nonparametric regression, sensitivity curve, two-way arrays
Week 7. Residuals analysis for two-way array, L1 approximation, median polish, diagnostic plot, "Data analysis and statistics: an expository overview"
Week 9. Exploratory analysis of variance: terminology, overlays, anova table, rob/res methods, examples.
Week 10. "Some principles of data analysis"
Week 11. r-squared, R-squared, Simpson's paradox, lurking variables
Week 12. Exploratory time series analysis (ETSA), plotting time series, methods
Week 13. Data mining - definitions. Contrasts with statistics
Week 14. Data mining for time series, for association rules, market basket analysis
Week 15. Review
12/7/04