Applying Contemporary Statistical Techniques


Applying Contemporary Statistical Techniques
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Applying Contemporary Statistical Techniques


Applying Contemporary Statistical Techniques
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Author : Rand R. Wilcox
language : en
Publisher: Elsevier
Release Date : 2003-01-16

Applying Contemporary Statistical Techniques written by Rand R. Wilcox and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-01-16 with Mathematics categories.


Applying Contemporary Statistical Techniques explains why traditional statistical methods are often inadequate or outdated when applied to modern problems. Wilcox demonstrates how new and more powerful techniques address these problems far more effectively, making these modern robust methods understandable, practical, and easily accessible. * Assumes no previous training in statistics * Explains how and why modern statistical methods provide more accurate results than conventional methods * Covers the latest developments on multiple comparisons * Includes recent advances in risk-based methods * Features many illustrations and examples using data from real studies * Describes and illustrates easy-to-use s-plus functions for applying cutting-edge techniques * Covers many contemporary ANOVA (analysis of variance) and regression methods not found in other books



Understanding And Applying Basic Statistical Methods Using R


Understanding And Applying Basic Statistical Methods Using R
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Author : Rand R. Wilcox
language : en
Publisher: John Wiley & Sons
Release Date : 2016-05-10

Understanding And Applying Basic Statistical Methods Using R written by Rand R. Wilcox and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-10 with Social Science categories.


Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians. Providing a conceptual basis for understanding the relative merits and applications of these methods, the book features modern insights and advances relevant to basic techniques in terms of dealing with non-normality, outliers, heteroscedasticity (unequal variances), and curvature. Featuring a guide to R, the book uses R programming to explore introductory statistical concepts and standard methods for dealing with known problems associated with classic techniques. Thoroughly class-room tested, the book includes sections that focus on either R programming or computational details to help the reader become acquainted with basic concepts and principles essential in terms of understanding and applying the many methods currently available. Covering relevant material from a wide range of disciplines, Understanding and Applying Basic Statistical Methods Using R also includes: Numerous illustrations and exercises that use data to demonstrate the practical importance of multiple perspectives Discussions on common mistakes such as eliminating outliers and applying standard methods based on means using the remaining data Detailed coverage on R programming with descriptions on how to apply both classic and more modern methods using R A companion website with the data and solutions to all of the exercises Understanding and Applying Basic Statistical Methods Using R is an ideal textbook for an undergraduate and graduate-level statistics courses in the science and/or social science departments. The book can also serve as a reference for professional statisticians and other practitioners looking to better understand modern statistical methods as well as R programming. Rand R. Wilcox, PhD, is Professor in the Department of Psychology at the University of Southern California, Fellow of the Association for Psychological Science, and an associate editor for four statistics journals. He is also a member of the International Statistical Institute. The author of more than 320 articles published in a variety of statistical journals, he is also the author eleven other books on statistics. Dr. Wilcox is creator of WRS (Wilcox’ Robust Statistics), which is an R package for performing robust statistical methods. His main research interest includes statistical methods, particularly robust methods for comparing groups and studying associations.



Fundamentals Of Modern Statistical Methods


Fundamentals Of Modern Statistical Methods
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Author : Rand R. Wilcox
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-21

Fundamentals Of Modern Statistical Methods written by Rand R. Wilcox and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-21 with Mathematics categories.


Conventional statistical methods routinely miss differences among groups or associations among variables. These differences are detected by more modern techniques. Hundreds of journal articles have described the reasons why standard techniques are unsatisfactory. Nonetheless, simple and intuitive explanations are generally unavailable. Without assuming any prior training in statistics, Part I of this book describes basic statistical principles from a point of view that makes their shortcomings easy to understand. Part II describes modern methods that address the problems covered in Part I. Using data from actual studies, many examples are included.



Modern Statistics For The Social And Behavioral Sciences


Modern Statistics For The Social And Behavioral Sciences
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Author : Rand Wilcox
language : en
Publisher: CRC Press
Release Date : 2011-08-05

Modern Statistics For The Social And Behavioral Sciences written by Rand Wilcox and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-05 with Mathematics categories.


In addition to learning how to apply classic statistical methods, students need to understand when these methods perform well, and when and why they can be highly unsatisfactory. Modern Statistics for the Social and Behavioral Sciences illustrates how to use R to apply both standard and modern methods to correct known problems with classic techniques. Numerous illustrations provide a conceptual basis for understanding why practical problems with classic methods were missed for so many years, and why modern techniques have practical value. Designed for a two-semester, introductory course for graduate students in the social sciences, this text introduces three major advances in the field: Early studies seemed to suggest that normality can be assumed with relatively small sample sizes due to the central limit theorem. However, crucial issues were missed. Vastly improved methods are now available for dealing with non-normality. The impact of outliers and heavy-tailed distributions on power and our ability to obtain an accurate assessment of how groups differ and variables are related is a practical concern when using standard techniques, regardless of how large the sample size might be. Methods for dealing with this insight are described. The deleterious effects of heteroscedasticity on conventional ANOVA and regression methods are much more serious than once thought. Effective techniques for dealing heteroscedasticity are described and illustrated. Requiring no prior training in statistics, Modern Statistics for the Social and Behavioral Sciences provides a graduate-level introduction to basic, routinely used statistical techniques relevant to the social and behavioral sciences. It describes and illustrates methods developed during the last half century that deal with known problems associated with classic techniques. Espousing the view that no single method is always best, it imparts a general understanding of the relative merits of various techniques so that the choice of method can be made in an informed manner.



Contemporary Statistical Models For The Plant And Soil Sciences


Contemporary Statistical Models For The Plant And Soil Sciences
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Author : Oliver Schabenberger
language : en
Publisher: CRC Press
Release Date : 2001-11-13

Contemporary Statistical Models For The Plant And Soil Sciences written by Oliver Schabenberger and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-11-13 with Mathematics categories.


Despite its many origins in agronomic problems, statistics today is often unrecognizable in this context. Numerous recent methodological approaches and advances originated in other subject-matter areas and agronomists frequently find it difficult to see their immediate relation to questions that their disciplines raise. On the other hand, statisticians often fail to recognize the riches of challenging data analytical problems contemporary plant and soil science provides. The first book to integrate modern statistics with crop, plant and soil science, Contemporary Statistical Models for the Plant and Soil Sciences bridges this gap. The breadth and depth of topics covered is unusual. Each of the main chapters could be a textbook in its own right on a particular class of data structures or models. The cogent presentation in one text allows research workers to apply modern statistical methods that otherwise are scattered across several specialized texts. The combination of theory and application orientation conveys ìwhyî a particular method works and ìhowî it is put in to practice. About the downloadable resources The accompanying downloadable resources are a key component of the book. For each of the main chapters additional sections of text are available that cover mathematical derivations, special topics, and supplementary applications. It supplies the data sets and SAS code for all applications and examples in the text, macros that the author developed, and SAS tutorials ranging from basic data manipulation to advanced programming techniques and publication quality graphics. Contemporary statistical models can not be appreciated to their full potential without a good understanding of theory. They also can not be applied to their full potential without the aid of statistical software. Contemporary Statistical Models for the Plant and Soil Science provides the essential mix of theory and applications of statistical methods pertinent to research in life sciences.



Statistical Techniques For Network Security Modern Statistically Based Intrusion Detection And Protection


Statistical Techniques For Network Security Modern Statistically Based Intrusion Detection And Protection
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Author : Wang, Yun
language : en
Publisher: IGI Global
Release Date : 2008-10-31

Statistical Techniques For Network Security Modern Statistically Based Intrusion Detection And Protection written by Wang, Yun and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-31 with Computers categories.


Provides statistical modeling and simulating approaches to address the needs for intrusion detection and protection. Covers topics such as network traffic data, anomaly intrusion detection, and prediction events.



Modern Statistical Methods For Hci


Modern Statistical Methods For Hci
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Author : Judy Robertson
language : en
Publisher: Springer
Release Date : 2016-03-22

Modern Statistical Methods For Hci written by Judy Robertson and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-22 with Computers categories.


This book critically reflects on current statistical methods used in Human-Computer Interaction (HCI) and introduces a number of novel methods to the reader. Covering many techniques and approaches for exploratory data analysis including effect and power calculations, experimental design, event history analysis, non-parametric testing and Bayesian inference; the research contained in this book discusses how to communicate statistical results fairly, as well as presenting a general set of recommendations for authors and reviewers to improve the quality of statistical analysis in HCI. Each chapter presents [R] code for running analyses on HCI examples and explains how the results can be interpreted. Modern Statistical Methods for HCI is aimed at researchers and graduate students who have some knowledge of “traditional” null hypothesis significance testing, but who wish to improve their practice by using techniques which have recently emerged from statistics and related fields. This book critically evaluates current practices within the field and supports a less rigid, procedural view of statistics in favour of fair statistical communication.



Statistics At Square Two


Statistics At Square Two
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Author : Michael J. Campbell
language : en
Publisher: John Wiley & Sons
Release Date : 2013-07-03

Statistics At Square Two written by Michael J. Campbell and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-03 with Medical categories.


Updated companion volume to the ever popular Statistics at Square One (SS1) Statistics at Square Two, Second Edition, helps you evaluate the many statistical methods in current use. Going beyond the basics of SS1, it covers sophisticated methods and highlights misunderstandings. Easy to read, it includes annotated computer outputs and keeps formulas to a minimum. Worked examples of methods such as multiple and logical regression reinforce the text. Each chapter concludes with exercises to stimulate learning. All those who need to understand statistics in clinical research papers and apply them in their own research will value this compact and coherent guide.



Modern Statistics With R


Modern Statistics With R
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Author : Måns Thulin
language : en
Publisher: BoD - Books on Demand
Release Date : 2021-07-28

Modern Statistics With R written by Måns Thulin and has been published by BoD - Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-28 with Mathematics categories.


The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. The aim of Modern Statistics with R is to introduce you to key parts of the modern statistical toolkit. It teaches you: - Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. - Exploratory data analysis - using visualisation and multivariate techniques to explore datasets. - Statistical inference - modern methods for testing hypotheses and computing confidence intervals. - Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. - Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. - Ethics in statistics - ethical issues and good statistical practice. - R programming - writing code that is fast, readable, and free from bugs. Starting from the very basics, Modern Statistics with R helps you learn R by working with R. Topics covered range from plotting data and writing simple R code to using cross-validation for evaluating complex predictive models and using simulation for sample size determination. The book includes more than 200 exercises with fully worked solutions. Some familiarity with basic statistical concepts, such as linear regression, is assumed. No previous programming experience is needed.



Applied Statistics


Applied Statistics
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Author : Dieter Rasch
language : en
Publisher: John Wiley & Sons
Release Date : 2019-10-07

Applied Statistics written by Dieter Rasch and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-07 with Mathematics categories.


Instructs readers on how to use methods of statistics and experimental design with R software Applied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry, public services, commerce, and research. It proceeds from a strong theoretical background, but it is practically oriented to develop one's ability to tackle new and non-standard problems confidently. Taking a practical approach to applied statistics, this user-friendly guide teaches readers how to use methods of statistics and experimental design without going deep into the theory. Applied Statistics: Theory and Problem Solutions with R includes chapters that cover R package sampling procedures, analysis of variance, point estimation, and more. It follows on the heels of Rasch and Schott's Mathematical Statistics via that book's theoretical background—taking the lessons learned from there to another level with this book’s addition of instructions on how to employ the methods using R. But there are two important chapters not mentioned in the theoretical back ground as Generalised Linear Models and Spatial Statistics. Offers a practical over theoretical approach to the subject of applied statistics Provides a pre-experimental as well as post-experimental approach to applied statistics Features classroom tested material Applicable to a wide range of people working in experimental design and all empirical sciences Includes 300 different procedures with R and examples with R-programs for the analysis and for determining minimal experimental sizes Applied Statistics: Theory and Problem Solutions with R will appeal to experimenters, statisticians, mathematicians, and all scientists using statistical procedures in the natural sciences, medicine, and psychology amongst others.