Exploring Data in Engineering, the Sciences, and Medicine

Hardcover | January 24, 2011

byRonald K. Pearson

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Two recent and ongoing developments have greatly increased both the range of opportunities for exploratory data analysis and the variety of tools to support this type of analysis. First has been the dramatic rise in the number of publicly available datasets available free from the Internetand second has been the similarly dramatic evolution of the Open Source software movement, making powerful analysis packages like R also freely available. The objective of this book is to provide a reasonably thorough introduction to a useful subset of these analysis tools, illustrating what theyare, what they do, and when and how they sometimes fail or do something very different than we expect them to. Specific topics covered include descriptive characterizations like summary statistics (mean, median, standard deviation, MAD scale estimate, etc.), graphical techniques like boxplots andnonparametric 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. In addition, thebook also introduces a variety of dynamic data analysis tools, including autocorrelation analysis, parametric and nonparametric spectrum estimation, and the use of nonlinear data cleaning filters to improve dynamic characterization results. The book assumes familiarity with calculus and linearalgebra, but does not assume any prior exposure to probability or statistics. Both simulation-based and real data examples are included and the book is intended either as an introductory textbook for an exploratory data analysis course like ones the author taught at the ETH where some of thismaterial was used, or for self-study. Exercises are included at the end of each chapter and both R code and datasets are available through the associated OUP website.

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Two recent and ongoing developments have greatly increased both the range of opportunities for exploratory data analysis and the variety of tools to support this type of analysis. First has been the dramatic rise in the number of publicly available datasets available free from the Internetand second has been the similarly dramatic evo...

Ronald 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 an...

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Format:HardcoverDimensions:768 pages, 9.25 × 6.13 × 0.98 inPublished:January 24, 2011Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0195089650

ISBN - 13:9780195089653

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Table of Contents

1. The Art of Analyzing2. Data: Types, Uncertainty and Quality3. Characterizing Categorical Variables4. Uncertainty in Real Variables5. Fitting Straight Lines6. A Brief Introduction to Estimation Theory7. Outliers: Distributional Monsters That Lurk in Data8. Characterizing a Dataset9. Confidence Intervals and Hypothesis Testing10. Associations between Variables11. Regression Models I: Real Data12. Re-expression: Data Transformations13. Regression Models II: Mixed Data Types14. Characterizing Analysis Results15. Regression Models III: Diagnostics and Refinements16. Dynamic Data Characterization17. Linear Data Filters18. Nonparametric Spectrum Estimation19. Irregularities in Dynamic Analysis20. Dealing with Missing Data