Idioma: Inglés
Publicado por Oxford University Press, 2011
ISBN 10: 0195089650 ISBN 13: 9780195089653
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Idioma: Inglés
Publicado por Oxford University Press, 2011
ISBN 10: 0195089650 ISBN 13: 9780195089653
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Idioma: Inglés
Publicado por Oxford University Press, 2011
ISBN 10: 0195089650 ISBN 13: 9780195089653
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Idioma: Inglés
Publicado por Oxford University Press, 2011
ISBN 10: 0195089650 ISBN 13: 9780195089653
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Publicado por Oxford University Press, 2011
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Idioma: Inglés
Publicado por Oxford University Press, 2011
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Idioma: Inglés
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Idioma: Inglés
Publicado por Oxford University Press, 2011
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Idioma: Inglés
Publicado por Oxford University Press Inc, New York, 2011
ISBN 10: 0195089650 ISBN 13: 9780195089653
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Añadir al carritoHardcover. Condición: 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Publicado por Oxford University Press, 2011
ISBN 10: 0195089650 ISBN 13: 9780195089653
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Idioma: Inglés
Publicado por Oxford University Press, USA, 2011
ISBN 10: 0195089650 ISBN 13: 9780195089653
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Idioma: Inglés
Publicado por Oxford University Press, 2011
ISBN 10: 0195089650 ISBN 13: 9780195089653
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Idioma: Inglés
Publicado por Oxford University Press, 2011
ISBN 10: 0195089650 ISBN 13: 9780195089653
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Idioma: Inglés
Publicado por Oxford University Press, 2011
ISBN 10: 0195089650 ISBN 13: 9780195089653
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Idioma: Inglés
Publicado por Oxford University Press Inc, New York, 2011
ISBN 10: 0195089650 ISBN 13: 9780195089653
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Añadir al carritoHardcover. Condición: 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.
Idioma: Inglés
Publicado por Oxford University Press, 2011
ISBN 10: 0195089650 ISBN 13: 9780195089653
Librería: preigu, Osnabrück, Alemania
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Añadir al carritoBuch. Condición: 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.
Idioma: Inglés
Publicado por Oxford University Press, 2011
ISBN 10: 0195089650 ISBN 13: 9780195089653
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoBuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.