Statistics 215a - Fall 2004 - D. R. Brillinger

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

brill@stat.Berkeley.EDU