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ePub Data Analysis by Resampling: Concepts and Applications download

by Clifford E. Lunneborg

ePub Data Analysis by Resampling: Concepts and Applications download
Author:
Clifford E. Lunneborg
ISBN13:
978-0534221102
ISBN:
0534221106
Language:
Publisher:
Duxbury Press; 1 edition (December 29, 1999)
Category:
Subcategory:
Mathematics
ePub file:
1740 kb
Fb2 file:
1760 kb
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Rating:
4.8
Votes:
213

PREFACE: DATA ANALYSIS BY RESAMPLING PART I: RESAMPLING CONCEPTS INTRODUCTION CONCEPTS 1: TERMS AND NOTATION Case, Attributes, Scores, and Treatments, Experimental and Observational Studies, Data Sets, Samples, and Populations, Parameters, Statistics.

PREFACE: DATA ANALYSIS BY RESAMPLING PART I: RESAMPLING CONCEPTS INTRODUCTION CONCEPTS 1: TERMS AND NOTATION Case, Attributes, Scores, and Treatments, Experimental and Observational Studies, Data Sets, Samples, and Populations, Parameters, Statistics, and Distributions, Distribution Functions APPLICATIONS 1: CASES, ATTRIBUTES, AND DISTRIBUTIONS Attributes, Scores, Groups, and Treatments, Distributions of Scores and Statistics, Exercises CONCEPTS 2: POPULATIONS AND RANDOM SAMPLES Varieties of Populations, Random Samples.

Request PDF On Jan 1, 2001, C. M. Anderson-Cook and others published Data Analysis by Resampling: Concepts and .

In DATA ANALYSIS BY RESAMPLING, Clifford Lunneborg argues that modern computing power has rendered the model-driven and assumption-plagued data analyses of the past unnecessary, obsolete, and inappropriate. This book introduces readers to modern, design-driven analyses that depend only on the observed data, on knowledge of how the data were collected, and on questions the In DATA ANALYSIS BY RESAMPLING, Clifford Lunneborg argues that modern computing power has rendered the model-driven and assumption-plagued data analyses of the past unnecessary, obsolete, and inappropriate.

Data Analysis by Resampling: Concepts and Applications. Clifford E. Lunneborg

Data Analysis by Resampling: Concepts and Applications. Lunneborg. PREFACE: DATA ANALYSIS BY RESAMPLING PART I: RESAMPLING CONCEPTS INTRODUCTION CONCEPTS 1: TERMS AND NOTATION Case, Attributes, Scores, and Treatments, Experimental and Observational Studies, Dat. More).

Coauthors & Alternates.

Elementary Multivariate Analysis for the Behavioural Sciences: Applications of Basic Structure. by Clifford E Lunneborg, Robert D. Abbott. Coauthors & Alternates. Learn More at LibraryThing. Clifford E Lunneborg at LibraryThing.

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The administration of the site is not responsible for the content of the site. All rights are reserved by their owners. Download book Data analysis by resampling : concepts and applications, Clifford E.

1999) Data Analysis by Resampling, Duxbury Press . Yu, Chong Ho (2003): Resampling methods: concepts, applications, and justification. statistical bootstrapping). Resampling: A Marriage of Computers and Statistics (ERIC Digests). Functions and datasets for bootstrapping from the book Bootstrap Methods and Their Applications by A. C. Davison and D. V. Hinkley (1997, CUP). Statistics101: Resampling, Bootstrap, Monte Carlo Simulation program. R package & Sampling Variance Estimation.

Clifford analysis, using Clifford algebras named after William Kingdon Clifford, is the study of Dirac operators, and Dirac type operators in analysis and geometry, together with their applications. Examples of Dirac type operators include, but are not limited to, the Hodge–Dirac operator, on a Riemannian manifold, the Dirac operator in euclidean space and its inverse on.

Data Analysis by Resampling by Clifford E. Bootstrap Methods: A Practitioner’s Guide by Michael R. Chernick. Modeling Longitudinal and Multilevel Data: Practical Issues, Applied Approaches, and Specific Examples by Todd Little, Kai Schnabel and Jorgen Baumert (2 copies).

In DATA ANALYSIS BY RESAMPLING, Clifford Lunneborg argues that modern computing power has rendered the model-driven and assumption-plagued data analyses of the past unnecessary, obsolete, and inappropriate. This book introduces readers to modern, design-driven analyses that depend only on the observed data, on knowledge of how the data were collected, and on questions the data were intended to answer. Overall, Lunneborg provides a modern and timely approach to statistical inference.
  • Professor Lunneborg covers bootstrap methods, permutation methods and subsampling methods and contrasts them in terms of the sampling design. This is a good introductory text at a fairly elementary level. Like Efron and Tibshirani (1993), Davison and Hinkley (1997) and Chernick (1999), he emphasizes the value of resampling in the age of modern fast computing and explores the variety of applications. This book is unique in that it could be used as an introductory text for students with only high school algebra. It also views the appropriateness of methods according to the experimenters sampling design. Use bootstrap when the data constitute random samples, permutation methods in randomized designs such as randomized trials and subsampling for non-random studies. While this is an interesting way to view the methods it is not universally accepted and both the bootstrap and the permutation tests have been applied in wider contexts.

  • I took several multivariate analysis courses from Dr. Lunneborg as a grad student at the University of Washington and they were pretty difficult being filled with matrix algebra and derivations. With that background, I was pleasantly surprised to find this book so well organized and applied. I do a great deal of resampling in my research and Dr. Lunneborg has done an excellent job of summarizing the various areas where resampling can save your butt, and where it can kick you in the butt if you are not careful. He provides the algorithms for Resampling Stats (a major resampling software package) and S-plus. I would have appreciated if he had included the code for Sas as well, but in most cases you can easily back it out from the S-plus code. If you are a student or an applied statistician and want to either learn how to use resampling techniques or actually apply it in your work, then this is an excellent book. If you are more mathematically oriented, then you would be better off going to the technical journals and reading the original works by Efron et al. to understand the logical, statistical and mathematical bases of this methodology. I have used the Resampling Stats Excel add-in for several years, so it was very useful to find a book that provides the algorithms for this software.