An Introduction to Modern Nonparametric Statistics

Author: James J. Higgins
Publisher: Duxbury Press
ISBN: 9780534387754
Format: PDF, Kindle
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Guided by problems that frequently arise in actual practice, James Higgins’ book presents a wide array of nonparametric methods of data analysis that researchers will find useful. It discusses a variety of nonparametric methods and, wherever possible, stresses the connection between methods. For instance, rank tests are introduced as special cases of permutation tests applied to ranks. The author provides coverage of topics not often found in nonparametric textbooks, including procedures for multivariate data, multiple regression, multi-factor analysis of variance, survival data, and curve smoothing. This truly modern approach teaches non-majors how to analyze and interpret data with nonparametric procedures using today’s computing technology.

Introduction to Modern Nonparametric Statistics

Author: CTI Reviews
Publisher: Cram101 Textbook Reviews
ISBN: 149702711X
Format: PDF, Docs
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Facts101 is your complete guide to Introduction to Modern Nonparametric Statistics. In this book, you will learn topics such as as those in your book plus much more. With key features such as key terms, people and places, Facts101 gives you all the information you need to prepare for your next exam. Our practice tests are specific to the textbook and we have designed tools to make the most of your limited study time.

A SAS Companion for Nonparametric Statistics

Author: Scott J. Richter
Publisher: Brooks/Cole
ISBN: 9780534422202
Format: PDF, Mobi
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This companion is designed for anyone who desires a guide to using SAS to carry out nonparametric analyses. It can serve as a SAS lab manual for students enrolled in a course covering nonparametric methods where SAS is used for computing. It is also an excellent reference for researchers who currently use SAS and wish to learn about the capabilities of SAS for performing nonparametric analyses and the syntax for implementing the procedures. An important feature of this companion is that all of the SAS examples presented are self-contained and can be entered into SAS, as they appear, and executed. Thus, the user does not have to deal with issues of creating SAS data sets before using the programs. In addition to presenting the SAS code to obtain various nonparametric analyses, brief introductions to the methods themselves are also given. Particular attention is given to how SAS calculates the results it presents, and explanation provided wherever SAS presents results that might be different from what is typically presented in textbooks.

Modern Business Statistics with Microsoft Excel

Author: David R. Anderson
Publisher: Cengage Learning
ISBN: 128596831X
Format: PDF, Mobi
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MODERN BUSINESS STATISTICS, 5E allows students to gain a strong conceptual understanding of statistics with a balance of real-world applications and a focus on the integrated strengths of Microsoft Excel 2013. To ensure student understanding, this best-selling, comprehensive text carefully discusses and clearly develops each statistical technique in a solid application setting. Microsoft Excel 2013 instruction, which is integrated in each chapter, plays an integral part in strengthening this edition's applications orientation. Immediately after each easy-to-follow presentation of a statistical procedure, a subsection discusses how to use Excel to perform the procedure. This integrated approach emphasizes the applications of Excel while focusing on the statistical methodology. Step-by-step instructions and screen captures further clarify student learning. A wealth of timely business examples, proven methods, and additional exercises throughout this edition demonstrate how statistical results provide insights into business decisions and present solutions to contemporary business problems. High-quality problems noted for their unwavering accuracy and the authors' signature problem-scenario approach clearly show how to apply statistical methods to practical business situations. New case problems and self-tests allow students to challenge their personal understanding. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Essentials of Modern Business Statistics with Microsoft Office Excel

Author: David R. Anderson
Publisher: Cengage Learning
ISBN: 1337516554
Format: PDF, Mobi
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Discover an accessible introduction to business statistics as ESSENTIALS OF MODERN BUSINESS STATISTICS, 7E balances a conceptual understanding of statistics with real-world applications of statistical methodology. The book integrates Microsoft Excel 2016, providing step-by-step instructions and screen captures to help readers master the latest Excel tools. Extremely reader-friendly, this edition includes numerous tools to maximize the user's success, including Self-Test Exercises, margin annotations, insightful Notes and Comments, and real-world Methods and Applications exercises. Eleven new Case Problems, as well as new Statistics in Practice applications and real data examples and exercises, give readers opportunities to put concepts into practice. Readers find everything needed to acquire key Excel 2016 skills and gain a strong understanding of business statistics. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Wahrscheinlichkeitstheorie und Stochastische Prozesse

Author: Michael Mürmann
Publisher: Springer-Verlag
ISBN: 364238160X
Format: PDF, Docs
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Dieses Lehrbuch beschäftigt sich mit den zentralen Gebieten einer maßtheoretisch orientierten Wahrscheinlichkeitstheorie im Umfang einer zweisemestrigen Vorlesung. Nach den Grundlagen werden Grenzwertsätze und schwache Konvergenz behandelt. Es folgt die Darstellung und Betrachtung der stochastischen Abhängigkeit durch die bedingte Erwartung, die mit der Radon-Nikodym-Ableitung realisiert wird. Sie wird angewandt auf die Theorie der stochastischen Prozesse, die nach der allgemeinen Konstruktion aus der Untersuchung von Martingalen und Markov-Prozessen besteht. Neu in einem Lehrbuch über allgemeine Wahrscheinlichkeitstheorie ist eine Einführung in die stochastische Analysis von Semimartingalen auf der Grundlage einer geeigneten Stetigkeitsbedingung mit Anwendungen auf die Theorie der Finanzmärkte. Das Buch enthält zahlreiche Übungen, teilweise mit Lösungen. Neben der Theorie vertiefen Anmerkungen, besonders zu mathematischen Modellen für Phänomene der Realität, das Verständnis.​

Nonparametric Methods in Statistics with SAS Applications

Author: Olga Korosteleva
Publisher: CRC Press
ISBN: 1466580631
Format: PDF
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Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods. The text begins with classical nonparametric hypotheses testing, including the sign, Wilcoxon sign-rank and rank-sum, Ansari-Bradley, Kolmogorov-Smirnov, Friedman rank, Kruskal-Wallis H, Spearman rank correlation coefficient, and Fisher exact tests. It then discusses smoothing techniques (loess and thin-plate splines) for classical nonparametric regression as well as binary logistic and Poisson models. The author also describes time-to-event nonparametric estimation methods, such as the Kaplan-Meier survival curve and Cox proportional hazards model, and presents histogram and kernel density estimation methods. The book concludes with the basics of jackknife and bootstrap interval estimation. Drawing on data sets from the author’s many consulting projects, this classroom-tested book includes various examples from psychology, education, clinical trials, and other areas. It also presents a set of exercises at the end of each chapter. All examples and exercises require the use of SAS 9.3 software. Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author’s website.

Vital statistics E Book

Author: Stephen McKenzie
Publisher: Elsevier Health Sciences
ISBN: 0729581497
Format: PDF, Mobi
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Vital Statistics: an introduction to health science statistics e-book is a new Australian publication. This textbook draws on real world, health-related and local examples, with a broad appeal to the Health Sciences student. It demonstrates how an understanding of statistics is useful in the real world, as well as in statistics exams. Vital Statistics: an introduction to health science statistics e-book is a relatively easy-to-read book that will painlessly introduce or re-introduce you to the statistical basics before guiding you through more demanding statistical challenges. Written in recognition of Health Sciences courses which require knowledge of statistical literacy, this book guides the reader to an understanding of why, as well as how and when to use statistics. It explores: How data relates to information, and how information relates to knowledge How to use statistics to distinguish information from disinformation The importance of probability, in statistics and in life That inferential statistics allow us to infer from samples to populations, and how useful such inferences can be How to appropriately apply and interpret statistical measures of difference and association How qualitative and quantitative methods differ, and when it’s appropriate to use each The special statistical needs of the health sciences, and some especially health science relevant statistics The vital importance of computers in the statistical analysis of data, and gives an overview of the most commonly used analyses Real-life local examples of health statistics are presented, e.g. A study conducted at the Department of Obstetrics and Gynecology, University of Utah School of Medicine, explored whether there might be a systematic bias affecting the results of genetic specimen tests, which could affect their generalizability. Reader-friendly writing style t-tests/ ANOVA family of inferential statistics all use variants of the same basic formula Learning Objectives at the start of each chapter and Quick Reference Summaries at the end of each chapter provide the reader with a scope of the content within each chapter.

Nonparametric Statistical Methods

Author: Myles Hollander
Publisher: John Wiley & Sons
ISBN: 1118553292
Format: PDF, Docs
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Praise for the Second Edition “This book should be an essential part of the personallibrary of every practicingstatistician.”—Technometrics Thoroughly revised and updated, the new edition of NonparametricStatistical Methods includes additional modern topics andprocedures, more practical data sets, and new problems fromreal-life situations. The book continues to emphasize theimportance of nonparametric methods as a significant branch ofmodern statistics and equips readers with the conceptual andtechnical skills necessary to select and apply the appropriateprocedures for any given situation. Written by leading statisticians, Nonparametric StatisticalMethods, Third Edition provides readers with crucialnonparametric techniques in a variety of settings, emphasizing theassumptions underlying the methods. The book provides an extensivearray of examples that clearly illustrate how to use nonparametricapproaches for handling one- or two-sample location and dispersionproblems, dichotomous data, and one-way and two-way layoutproblems. In addition, the Third Edition features: The use of the freely available R software to aid incomputation and simulation, including many new R programs writtenexplicitly for this new edition New chapters that address density estimation, wavelets,smoothing, ranked set sampling, and Bayesian nonparametrics Problems that illustrate examples from agricultural science,astronomy, biology, criminology, education, engineering,environmental science, geology, home economics, medicine,oceanography, physics, psychology, sociology, and spacescience Nonparametric Statistical Methods, Third Edition is anexcellent reference for applied statisticians and practitioners whoseek a review of nonparametric methods and their relevantapplications. The book is also an ideal textbook forupper-undergraduate and first-year graduate courses in appliednonparametric statistics.