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OverviewThe recent dramatic rise in the number of public datasets available free from the Internet, coupled with the evolution of the Open Source software movement, which makes powerful analysis packages like R freely available, have greatly increased both the range of opportunities for exploratory data analysis and the variety of tools that support this type of analysis. This book will provide a thorough introduction to a useful subset of these analysis tools, illustrating what they are, what they do, and when and how they fail. Specific topics covered include descriptive characterizations like summary statistics (mean, median, standard deviation, MAD scale estimate), graphical techniques like boxplots and nonparametric density estimates, various forms of regression modeling (standard linear regression models, logistic regression, and highly robust techniques like least trimmed squares), and the recognition and treatment of important data anomalies like outliers and missing data. The unique combination of topics presented in this book separate it from any other book of its kind. Intended for use as an introductory textbook for an exploratory data analysis course or as self-study companion for professionals and graduate students, this book assumes familiarity with calculus and linear algebra, though no previous exposure to probability or statistics is required. Both simulation-based and real data examples are included, as are end-of-chapter exercises and both R code and datasets. Full Product DetailsAuthor: Ronald Pearson (Senior Statistician, Senior Statistician, The Travelers Companies)Publisher: Oxford University Press Inc Imprint: Oxford University Press Inc Dimensions: Width: 23.60cm , Height: 4.30cm , Length: 16.30cm Weight: 1.220kg ISBN: 9780195089653ISBN 10: 0195089650 Pages: 792 Publication Date: 03 February 2011 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsContents 1. The Art of Analyzing Data 2. Data: Types, Uncertainty and Quality 3. Characterizing Categorical Variables 4. Uncertainty in Real Variables 5. Fitting Straight Lines 6. A Brief Introduction to Estimation Theory 7. Outliers: Distributional Monsters (?) That Lurk in Data 8. Characterizing a Dataset 9. Confidence Intervals and Hypothesis Testing 10. Relations among Variables 11. Regression Models I: Real Data 12. Reexpression: Data Transformations 13. Regression Models II: Mixed Data Types 14. Characterizing Analysis Results 15. Regression Models III: Diagnostics and Refinements 16. Dealing with Missing DataReviewsAuthor InformationRonald Pearson has held a wide variety of technical positions in both academia and industry, including the DuPont Company, the Swiss Federal Institute of Technology (ETH, Zurich), the Tampere University of Technology in Tampere, Finland, and most recently, the Travelers Companies. Dr. Pearson's experience has included the analysis and modeling of industrial process operating data, the design of nonlinear digital filters for data cleaning applications, the analysis of historical clinical data, and he is currently involved in developing models for predictive analytics applied to large business datasets. His research interests include model structure selection for nonlinear discrete-time dynamic models of empirical data, the algebraic characterization and design of nonlinear digital filters, and the development of exploratory data analysis techniques for large datasets involving mixed data types. Tab Content 6Author Website:Countries AvailableAll regions |
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