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Introduction to Statistics: The Nonparametric Way (Springer Texts in Statistics) download ebook

by G.E. Noether

Introduction to Statistics: The Nonparametric Way (Springer Texts in Statistics) download ebook
ISBN:
3540972846
ISBN13:
978-3540972846
Author:
G.E. Noether
Publisher:
Springer-Verlag Berlin and Heidelberg GmbH & Co. K (December 31, 1991)
Language:
Pages:
490 pages
ePUB:
1762 kb
Fb2:
1386 kb
Other formats:
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Category:
Mathematics
Subcategory:
Rating:
4.3

Series: Springer Texts in Statistics. I demonstrated this in my introductory biostatistics text that was published in 2002

Series: Springer Texts in Statistics. I demonstrated this in my introductory biostatistics text that was published in 2002. Much before me Noether had been unconventional also by teaching nonparametric statistics as the basis for an introduction to statistics. This was not popular among statisticians who favored teaching traditional parametric approaches and liked to religate nonparametrics to advanced courses. I think Noether had the right idea and he developed his approach through an earlier version of this work. I consider this an important classic text in basic statistics.

The present text, which is the successor to the author's Introduction to Statistics: A Nonparametric Approach (Houghton Mifflin Company, Boston, 1976), tries to meet these objectives by introducing the student to the ba­ sic ideas of estimation and hypothesis testing early in the course after a rather brief introduction to data organization and some simple ideas about probability.

Springer Texts in Statistics. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning. The eld encompasses many methods such as the lasso and sparse regression, classication and regression trees, and boosting and support vector machines. 1. 2 1. Introduction.

Introduction to Statistics - The Nonparametric Way, Gottfried E. Noether (1991) Statistics in Scientific Investigation - Its Basis, Application, and Interpretation, Glen McPherson (1990). Noether (1991). Statistical Methods: The Geometric Approach, David J. Saville & Graham R. Wood (1991). Regression Analysis - Theory, Methods, and Applications, Ashish Sen & Muni Srivastava (1990) Statistics in Scientific Investigation - Its Basis, Application, and Interpretation, Glen McPherson (1990). Regression Analysis - Theory, Methods and Applications, Ashish Sen & Muni Srivastava (1990). Fundamentals of Mathematical Statistics - Probability for Statistics, Hung T. Nguyen & Gerald S. Rogers (1989). Alfred: Elements of Statistics for the Life and Social Sciences Berger: An Introduction to Probability and Stochastic Processes Bilodeau and Brenner: Theory of Multivariate Statistics Blom: Probability and Statistics: Theory and Applications Brockwell and Davis: Introduction to Times Series and Forecasting, Second. A Modern Introduction to Probability and Statistics. Introduction to Python for Econometrics, Statistics and Data Analysis. 54 MB·14,434 Downloads Introduction to Statistics and Data Analysis : With Exercises, Solutions and Applications in R. 457 Pages·2016·8. Introduction to the Practice of Statistics. Introduction to Probability and Mathematical Statistics. 36 MB·2,631 Downloads·New!

Introduction to Statistics book. Goodreads helps you keep track of books you want to read. Start by marking Introduction to Statistics: The Nonparametric Way as Want to Read: Want to Read savin. ant to Read.

Introduction to Statistics book. Chapter · January 2013 with 5 Reads Nonparametric Methods in Multivariate Analysis. Chapter · January 2013 with 5 Reads. How we measure 'reads'. This book is a guide to using S-Plus to perform statistical analyses and provides both an introduction to the use of S-Plus and a course in modern statistical methods. The aim of the book is to show how to use S-Plus as a powerful and graphical system. Nonparametric Methods in Multivariate Analysis.

Robert H. Shumway Department of Statistics University of California Davis, California USA Prof. This book is designed to be useful as a text for courses in time series on several different levels and as a reference work for practitioners facing the analysis of timecorrelated data in the physical, biological, and social sciences. We have used earlier versions of the text at both the undergraduate and graduate levels over the past decade.

Springer texts in statistics). Includes bibliographical references and index. These materials are in the Statistics Department’s P. qualifying examination syllabus

Springer texts in statistics). ISBN 0-387-95382-5 (alk. paper). This book is intended for a course entitled Mathematical Statistics oered at the Department of Statistics, University of Wisconsin-Madison. This course, taught in a mathematically rigorous fashion, covers essential ma-terials in statistical theory that a rst or second year graduate student typically needs to learn as preparation for work on a P. degree in statis-tics. qualifying examination syllabus. This part of the book is inuenced by several standard textbooks, such as Casella and. vii.

The text introduces the student to the basic ideas of estimation and hypothesis testing early in the course after a rather brief introduction to data organization and some simple ideas about probability. Estimation and hypothesis testing are discussed in terms of the two-sample problem. The book exploits nonparametric ideas that rely on nothing more complicated than sample differences Y-X, referred to as elementary estimates, to define the Wilcoxon-Mann-Whitney test statistics and the related point and interval estimates. The ideas behind elementary estimates are then applied to the one-sample problem and to linear regression and rank correlation. Discussion of the Kruskal-Wallis and Friedman procedures for the k-sample problem rounds out the nonparametric coverage. The concluding chapters provide a discussion of Chi-square tests for the analysis of categorical data and introduce the student to the analysis of binomial data including the computation of power and sample size. Most chapters in the book have an appendix discussing relevant Minitab commands.