Exploring Data Engineering Sciences by Pearson Ronald (20 results)

Hardcover. Condition: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Hardback. Condition: Very Good. 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 Internet and 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 they are, 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 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. In addition, the book 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 linear algebra, 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 this material 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.…

Hardback. Condition: Very Good. 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 Internet and 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 they are, 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 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. In addition, the book 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 linear algebra, 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 this material 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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Condition: New. Num Pages: 792 pages, 250 line illustrations. BIC Classification: PBT; TBM. Category: (P) Professional & Vocational. Dimension: 240 x 164 x 44. Weight in Grams: 1230. . 2011. Illustrated. hardcover. . . . .

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Condition: New. Num Pages: 792 pages, 250 line illustrations. BIC Classification: PBT; TBM. Category: (P) Professional & Vocational. Dimension: 240 x 164 x 44. Weight in Grams: 1230. . 2011. Illustrated. hardcover. . . . . Books ship from the US and Ireland.

Hardback. Condition: New. 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 Internet and 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 they are, 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 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. In addition, the book 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 linear algebra, 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 this material 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.…

Hardback. Condition: New. 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 Internet and 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 they are, 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 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. In addition, the book 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 linear algebra, 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 this material 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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Hardcover. Condition: new. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Buch. Condition: Neu. Exploring Data in Engineering, the Sciences, and Medicine | Ronald K. Pearson | Buch | Gebunden | Englisch | 2011 | Oxford University Press | EAN 9780195089653 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Condition: New. Print on Demand pp. 768.

Condition: New. Print on Demand pp. 768.

Condition: New. PRINT ON DEMAND pp. 768.