Exploring Data in Engineering, the Sciences, and Medicine (Hardcover)

Ronald Pearson

ISBN 10: 0195089650 / ISBN 13: 9780195089653

Published by Oxford University Press Inc, New York, 2011

About this Item

Hardcover. The 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 theseanalysis 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 introductorytextbook 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 previousexposure 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. This book introduces various widely available exploratory data analysis methods, emphasizing those that are most useful in the preliminary exploration of large datasets involving mixed data types. Topics include descriptive statistics, graphical analysis tools, regression modeling and spectrum estimation, along with practical issues like outliers, missing data, and variable selection. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9780195089653

Bibliographic Details

• Title: Exploring Data in Engineering, the Sciences, and Medicine (Hardcover)
• Publisher: Oxford University Press Inc, New York
• Publication Date: 2011
• Language: English
• ISBN 10: 0195089650
• ISBN 13: 9780195089653
• Binding: Hardcover
• Condition: new

About this title

Synopsis

The 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.

About the Author

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 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.