Exploring data tables, trends, and shapes /

1 Theories of Data Analysis: From Magical Thinking Through Classical Statistics Persi Diaconis p. 1
1A Intuitive Statistics-Some Inferential Problems p. 4
1B Multiplicity-A Pervasive Problem p. 9
1C Some Remedies p. 12
1D Theories for Data Analysis p. 22
1E Uses for Mathematics p. 29
1F In Defense of Controlled Magical Thinking p. 31
2 Fitting by Organized Comparisons: The Square Combining Table Katherine Godfrey p. 37
2A Combining Comparisons p. 37
2B Two-Way Tables p. 39
2C Paired Comparisons p. 47
2D Analyzing Tables Containing Holes p. 49
2E Summary p. 61
Exercises p. 62
3 Resistant Nonadditive Fits for Two-Way Tables John D. Emerson and George Y. Wong p. 67
3A The Simple Additive Model and Median Polish p. 68
3B One Step Beyond an Additive Fit p. 71
3C Assessing and Comparing Fits p. 79
3D Multiplicative Fits p. 83
3E Techniques for Obtaining Simple Multiplicative Fits p. 92
3F Additive-Plus-Multiplicative Fits p. 100
3G Some Background for Nonadditive Fits p. 113
3H Summary p. 117
Exercises p. 119
4 Three-Way Analyses Nancy Romanowicz Cook p. 125
4A Structure of the Three-Way Table p. 126
4B Decompositions and Models for Three-Way Analysis p. 128
4C Median-Polish Analysis for the Main-Effects-Only Case p. 130
4D Nonadditivity and a Diagnostic Plot in Main-Effects-Only Analysis p. 145
4E Analysis Using Means p. 158
4F Median-Polish Analysis for the Full-Effects Case p. 164
4G Diagnostic Plots for the Full-Effects Case p. 176
4H Fitting the Full-Effects Model by Means p. 180
4I Computation, Other Polishes, and Missing Values p. 182
4J Summary p. 183
Exercises p. 185
5 Identifying Extreme Cells in a Sizable Contingency Table: Probabilistic and Exploratory Approaches Frederick Mosteller and Anita Parunak p. 189
5A The Hypergeometric Distribution p. 192
5B Assessing Outliers p. 195
5C The Simulation Approach p. 199
5D Applying the Simulation Approach to the Table of Archaeological Data p. 206
5E An Exploratory Approach, Based on Deviations from Independence p. 212
5F A Logarithmic Exploratory Approach p. 214
5G Illustrations of the New Standardization p. 217
5H Summary p. 221
5I Conclusion p. 223
6 Fitting Straight Lines by Eye Frederick Mosteller and Andrew F. Siegel and Edward Trapido and Cleo Youtz p. 225
6A Method p. 226
6B Results p. 229
6C Summary p. 238
7 Resistant Multiple Regression, One Variable at a Time John D. Emerson and David C. Hoaglin p. 241
7A Resistant Lines p. 242
7B Sweeping Out p. 246
7C Example p. 250
7D When Carriers Come in Blocks p. 263
7E Summary p. 273
Exercises p. 275
8 Robust Regression Guoying Li p. 281
8A Why Robust Regression? p. 282
8B M-Estimators and W-Estimators for Regression p. 291
8C Computation p. 304
8D Example: The Stack Loss Data p. 310
8E Bounded-Influence Regression p. 322
8F Some Alternative Methods p. 328
8G Summary p. 335
Exercises p. 341
9 Checking the Shape of Discrete Distributions David C. Hoaglin and John W. Tukey p. 345
9A A Poissonness Plot p. 348
9B Confidence Intervals for the Count Metameter p. 358
9C When Is a Point Discrepant? p. 370
9D Overall Plots for Other Families of Distributions p. 376
9E Frequency-Ratio Alternatives p. 389
9F Cooperative Diversity p. 396
9G Double-Root Residuals p. 406
9H Summary p. 409
Exercises p. 412
10 Using Quantiles to Study Shape David C. Hoaglin p. 417
10A Diagnosing Skewness p. 419
10B Diagnosing Elongation p. 425
10C Quantile-Quantile Plots p. 432
10D Plots for Skewness and Elongation p. 442
10E Pushback Analysis p. 450
10F Summary p. 454
10G Appendix p. 456
Exercises p. 459
11 Summarizing Shape Numerically: The g-And-h Distributions David C. Hoaglin p. 461
11A Skewness p. 462
11B Elongation p. 479
11C Combining Skewness and Elongation p. 485
11D More General Patterns of Skewness and Elongation p. 490
11E Working from Frequency Distributions p. 496
11F Moments p. 501
11G Other Approaches to Shape p. 504
11H Summary p. 508
Exercises p. 511
Index p. 515