Zustand: Good. 0th Edition. Used book that is in clean, average condition without any missing pages.
Anbieter: World of Books (was SecondSale), Montgomery, IL, USA
Zustand: Good. Item in good condition and has highlighting/writing on text. Used texts may not contain supplemental items such as CDs, info-trac etc.
Sprache: Englisch
Verlag: Burr Ridge, Illinois, U.S.A.: Richard d Irwin, 1983
ISBN 10: 0256028486 ISBN 13: 9780256028485
Anbieter: Bingo Books 2, Vancouver, WA, USA
Soft cover. Zustand: Near Fine. soft cover in near fine condition.
Sprache: Englisch
Verlag: Hodder Education Publishers, 2005
ISBN 10: 0340760842 ISBN 13: 9780340760840
Anbieter: 3rd St. Books, Lees Summit, MO, USA
Erstausgabe
Soft cover. Zustand: Very Good. 1st Edition. Very good, clean, tight condition. Text free of marks. Professional book dealer since 1999. All orders are processed promptly and carefully packaged.
Anbieter: MB Books, Derbyshire, Vereinigtes Königreich
EUR 9,46
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In den WarenkorbSoft cover. Zustand: Good. No Jacket. Condition : Good. Soft cover, no jacket. 232pp. No highlighting or annotations to main text. Photo on request.
Sprache: Englisch
Verlag: Hodder Stoughton Educational, 2005
Anbieter: Books in my Basket, New Delhi, Indien
Soft cover. Zustand: New. ISBN:9780340760840.
Anbieter: Lucky's Textbooks, Dallas, TX, USA
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In den WarenkorbPaperback. Zustand: New.
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Zustand: Very good.
Zustand: New. pp. 301.
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Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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EUR 112,39
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In den WarenkorbPF. Zustand: New.
Zustand: New.
Sprache: Englisch
Verlag: Springer Nature Singapore, Springer Nature Singapore Apr 2018, 2018
ISBN 10: 9811095957 ISBN 13: 9789811095955
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. Neuware -This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 316 pp. Englisch.
Sprache: Englisch
Verlag: Springer Nature Singapore, Springer Nature Singapore, 2018
ISBN 10: 9811095957 ISBN 13: 9789811095955
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter.This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on singular value decomposition among theorems in matrix algebra.The book begins with an explanation of fundamental matrix operations and the matrix expressions of elementary statistics, followed by the introduction of popular multivariate procedures with advancing levels of matrix algebra chapter by chapter. This organization of the book allows readers without knowledge of matrices to deepen their understanding of multivariate data analysis.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 147,01
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In den WarenkorbZustand: New. In.
Anbieter: Lucky's Textbooks, Dallas, TX, USA
EUR 166,49
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In den WarenkorbZustand: New.
Zustand: New.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 172,23
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In den WarenkorbPaperback. Zustand: Brand New. 6th edition edition. 480 pages. 10.00x8.00x1.13 inches. In Stock.
Sprache: Englisch
Verlag: Springer Nature Singapore, Springer Nature Singapore Mai 2021, 2021
ISBN 10: 9811541051 ISBN 13: 9789811541056
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. Neuware -This is the first textbook that allows readers who may be unfamiliar with matrices to understand a variety of multivariate analysis procedures in matrix forms. By explaining which models underlie particular procedures and what objective function is optimized to fit the model to the data, it enables readers to rapidly comprehend multivariate data analysis. Arranged so that readers can intuitively grasp the purposes for which multivariate analysis procedures are used, the book also offers clear explanations of those purposes, with numerical examples preceding the mathematical descriptions.Supporting the modern matrix formulations by highlighting singular value decomposition among theorems in matrix algebra, this book is useful for undergraduate students who have already learned introductory statistics, as well as for graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis.The book begins by explainingfundamental matrix operations and the matrix expressions of elementary statistics. Then, it offers an introduction to popular multivariate procedures, with each chapter featuring increasing advanced levels of matrix algebra.Further the book includes in six chapters on advanced procedures, covering advanced matrix operations and recently proposed multivariate procedures, such as sparse estimation, together with a clear explication of the differences between principal components and factor analyses solutions. In a nutshell, this book allows readers to gain an understanding of the latest developments in multivariate data science.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 480 pp. Englisch.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 180,50
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In den WarenkorbPaperback. Zustand: Brand New. 2nd edition. 480 pages. 9.25x6.10x1.13 inches. In Stock.
Sprache: Englisch
Verlag: Springer Nature Singapore, Springer Nature Singapore, 2021
ISBN 10: 9811541051 ISBN 13: 9789811541056
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This is the first textbook that allows readers who may be unfamiliar with matrices to understand a variety of multivariate analysis procedures in matrix forms. By explaining which models underlie particular procedures and what objective function is optimized to fit the model to the data, it enables readers to rapidly comprehend multivariate data analysis. Arranged so that readers can intuitively grasp the purposes for which multivariate analysis procedures are used, the book also offers clear explanations of those purposes, with numerical examples preceding the mathematical descriptions.Supporting the modern matrix formulations by highlighting singular value decomposition among theorems in matrix algebra, this book is useful for undergraduate students who have already learned introductory statistics, as well as for graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis.The book begins by explainingfundamental matrix operations and the matrix expressions of elementary statistics. Then, it offers an introduction to popular multivariate procedures, with each chapter featuring increasing advanced levels of matrix algebra. Further the book includes in six chapters on advanced procedures, covering advanced matrix operations and recently proposed multivariate procedures, such as sparse estimation, together with a clear explication of the differences between principal components and factor analyses solutions. In a nutshell, this book allows readers to gain an understanding of the latest developments in multivariate data science.
Zustand: New.
Anbieter: Books Puddle, New York, NY, USA
Zustand: New.
Anbieter: Mispah books, Redhill, SURRE, Vereinigtes Königreich
EUR 188,05
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In den WarenkorbPaperback. Zustand: New. New. book.
Sprache: Englisch
Verlag: Springer Nature Singapore, Springer Nature Singapore Mai 2020, 2020
ISBN 10: 9811541027 ISBN 13: 9789811541025
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Buch. Zustand: Neu. Neuware -This is the first textbook that allows readers who may be unfamiliar with matrices to understand a variety of multivariate analysis procedures in matrix forms. By explaining which models underlie particular procedures and what objective function is optimized to fit the model to the data, it enables readers to rapidly comprehend multivariate data analysis. Arranged so that readers can intuitively grasp the purposes for which multivariate analysis procedures are used, the book also offers clear explanations of those purposes, with numerical examples preceding the mathematical descriptions.Supporting the modern matrix formulations by highlighting singular value decomposition among theorems in matrix algebra, this book is useful for undergraduate students who have already learned introductory statistics, as well as for graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis.The book begins by explainingfundamental matrix operations and the matrix expressions of elementary statistics. Then, it offers an introduction to popular multivariate procedures, with each chapter featuring increasing advanced levels of matrix algebra.Further the book includes in six chapters on advanced procedures, covering advanced matrix operations and recently proposed multivariate procedures, such as sparse estimation, together with a clear explication of the differences between principal components and factor analyses solutions. In a nutshell, this book allows readers to gain an understanding of the latest developments in multivariate data science.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 480 pp. Englisch.
Sprache: Englisch
Verlag: Springer Nature Singapore, Springer Nature Singapore, 2020
ISBN 10: 9811541027 ISBN 13: 9789811541025
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This is the first textbook that allows readers who may be unfamiliar with matrices to understand a variety of multivariate analysis procedures in matrix forms. By explaining which models underlie particular procedures and what objective function is optimized to fit the model to the data, it enables readers to rapidly comprehend multivariate data analysis. Arranged so that readers can intuitively grasp the purposes for which multivariate analysis procedures are used, the book also offers clear explanations of those purposes, with numerical examples preceding the mathematical descriptions.Supporting the modern matrix formulations by highlighting singular value decomposition among theorems in matrix algebra, this book is useful for undergraduate students who have already learned introductory statistics, as well as for graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis.The book begins by explainingfundamental matrix operations and the matrix expressions of elementary statistics. Then, it offers an introduction to popular multivariate procedures, with each chapter featuring increasing advanced levels of matrix algebra. Further the book includes in six chapters on advanced procedures, covering advanced matrix operations and recently proposed multivariate procedures, such as sparse estimation, together with a clear explication of the differences between principal components and factor analyses solutions. In a nutshell, this book allows readers to gain an understanding of the latest developments in multivariate data science.