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<b>1. Theories of Data Analysis: From Magical Thinking Through Classical Statistics 1<br /> </b><i>Peris Diaconis</i> <p>1A. Intuitive Statistics—Some Inferential Problems 4</p> <p>IB. Multiplicity—A Pervasive Problem 9</p> <p>1C. Some Remedies 12</p> <p>ID. Theories for Data Analysis 22</p> <p>IE. Uses for Mathematics 29</p> <p>IF. In Defense of Controlled Magical Thinking 31</p> <p><b>2. Fitting by Organized Comparisons: The Square Combining Table 37</b><br /> <i>Katherine Godfrey</i></p> <p>2A. Combining Comparisons 37</p> <p>2B. Two-Way Tables 39</p> <p>2C. Paired Comparisons 47</p> <p>2D. Analyzing Tables Containing Holes 49</p> <p>2E. Summary 61</p> <p><b>3. Resistant Nonadditive Fits for Two-Way Tables 67</b><br /> <i>John D. Emerson and Gregory Y. Wong</i></p> <p>3A. The Simple Additive Model and Median Polish 68</p> <p>3B. One Step Beyond an Additive Fit 71</p> <p>3C. Assessing and Comparing Fits 79</p> <p>3D. Multiplicative Fits 83</p> <p>3E. Techniques for Obtaining Simple Multiplicative Fits 92</p> <p>3F. Additive-Plus-Multiplicative Fits 100</p> <p>3G. Some Background for Nonadditive Fits 113</p> <p>3H. Summary 117</p> <p><b>4. Three-Way Analysis 125</b><br /> <i>Nancy Cook</i></p> <p>4A. Structure of the Three-Way Table 126</p> <p>4B. Decompositions and Models for Three-Way Analysis 128</p> <p>4C. Median-Polish Analysis for the Main-Effects-Only Case 130</p> <p>4D. Nonadditivity and a Diagnostic Plot in Main-Effects-Only Analysis 145</p> <p>4E. Analysis Using Means 158</p> <p>4F. Median-Polish Analysis for the Full-Effects Case 164</p> <p>4G. Diagnostic Plots for the Full-Effects Case 176</p> <p>4H. Fitting the Full-Effects Model by Means 180</p> <p>4I. Computation, Other Polishes, and Missing Values 182</p> <p>4J. Summary 183</p> <p><b>5. Identifying Extreme Cells in a Sizable Contingency Table: Probabilistic and Exploratory Approaches 189</b><br /> <i>Frederick Mosteller and Anita Parunak</i></p> <p>5A. The Hypergeometric Distribution 192</p> <p>5B. Assessing Outliers 195</p> <p>5C. The Simulation Approach 199</p> <p>5D. Applying the Simulation Approach to the Table of Archaeological Data 206</p> <p>5E. An Exploratory Approach, Based on Deviations from Independence 212</p> <p>5F. A Logarithmic Exploratory Approach 214</p> <p>5G. Illustrations of the New Standardization 217</p> <p>5H. Summary 221</p> <p>51. Conclusion 223</p> <p><b>6. Fitting Straight Lines By Eye 225</b><br /> <i>Frederick Mosteller, Andrew F. Siegel, Edward Trapido, and Cleo Youtz</i></p> <p>6A. Method 226</p> <p>6B. Results 229</p> <p>6C. Summary 238</p> <p><b>7. Resistant Multiple Regression, One Variable at a Time 241</b><br /> <i>John D. Emerson and David C. Hoaglin</i></p> <p>7A. Resistant Lines 242</p> <p>7B. Sweeping Out 246</p> <p>7C. Example 250</p> <p>7D. When Carriers Come in Blocks 263</p> <p>7E. Summary 273</p> <p><b>8. Robust Regression 281</b><br /> <i>Guoying Li</i></p> <p>8A. Why Robust Regression? 282</p> <p>8B. <i>M</i>-Estimators and W-Estimators for Regression 291</p> <p>8C. Computation 304</p> <p>8D. Example: The Stack Loss Data 310</p> <p>8E. Bounded-Influence Regression 322</p> <p>8F. Some Alternative Methods 328</p> <p>8G. Summary 335</p> <p><b>9. Checking the Shape of Discrete Distributions 345</b><br /> <i>David C. Hoaglin and John W. Tukey</i></p> <p>9A. A Poissonness Plot 348</p> <p>9B. Confidence Intervals for the Count Metameter 358</p> <p>9C. When Is a Point Discrepant? 370</p> <p>9D. Overall Plots for Other Families of Distributions 376</p> <p>9E. Frequency-Ratio Alternatives 389</p> <p>9F. Cooperative Diversity 396</p> <p>9G. Double-Root Residuals 406</p> <p>9H. Summary 409</p> <p><b>10. Using Quantiles to Study Shape 417</b><br /> <i>David C. Hoaglin</i></p> <p>10A. Diagnosing Skewness 419</p> <p>10B. Diagnosing Elongation 425</p> <p>IOC. Quantile-Quantile Plots 432</p> <p>10D. Plots for Skewness and Elongation 442</p> <p>10E. Pushback Analysis 450</p> <p>10F. Summary 454</p> <p>10G. Appendix 456</p> <p><b>11. Summarizing Shape Numerically: The g-and-h Distributions 416</b><br /> <i>David C. Hoaglin</i></p> <p>11 A. Skewness 462</p> <p>11B. Elongation 479</p> <p>11C. Combining Skewness and Elongation 485</p> <p>11D. More General Patterns of Skewness and Elongation 490</p> <p>HE. Working from Frequency Distributions 496</p> <p>11F. Moments 501</p> <p>11G. Other Approaches to Shape 504</p> <p>11H. Summary 508</p> <p><b>Index.</b></p>

<p><b>DAVID C. HOAGLIN</b>, PhD, is a Fellow of the American Statistical Association.</p> <p><b>FREDERICK MOSTELLER</b>, PhD, has been the recipient of several honorary degrees and is a former President of the American Statistical Association.</p> <p><b>JOHN W. TUKEY</b>, PhD, has received the National Medal of Science as well as several honorary degrees.</p>

WILEY-INTERSCIENCE PAPERBACK SERIES<br /> <br /> <p>The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.</p> <p>"Exploring Data Tables, Trends, and Shapes (EDTTS) was written as a companion volume to the same editors' book, Understanding Robust and Exploratory Data Analysis (UREDA). Whereas UREDA is a collection of exploratory and resistant methods of estimation and display, EDTTS goes a step further, describing multivariate and more complicated techniques . . . I feel that the authors have made a very significant contribution in the area of multivariate nonparametric methods. This book [is] a valuable source of reference to researchers in the area."<br /> —Technometrics</p> <p>"This edited volume . . . provides an important theoretical and philosophical extension to the currently popular statistical area of Exploratory Data Analysis, which seeks to reveal structure, or simple descriptions, in data . . . It is . . . an important reference volume which any statistical library should consider seriously."<br /> —The Statistician</p> <p>This newly available and affordably priced paperback version of Exploring Data Tables, Trends, and Shapes presents major advances in exploratory data analysis and robust regression methods and explains the techniques, relating them to classical methods. The book addresses the role of exploratory and robust techniques in the overall data-analytic enterprise, and it also presents new methods such as fitting by organized comparisons using the square combining table and identifying extreme cells in a sizable contingency table with probabilistic and exploratory approaches. The book features a chapter on using robust regression in less technical language than available elsewhere. Conceptual support for each technique is also provided.</p>

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