Psychological Statistics and Psychometrics Using Stata is a complete and concise resource for students and researchers in the behavioral sciences. The author's primary goal in this book is to help readers become competent users of statistics. To that end, he first introduces basic statistical methods such as regression, t tests, and ANOVA. He focuses on explaining the models, how they can be used with different types of variables, and how to interpret the results. After building this foundation, the author covers more advanced statistical techniques, including power-and-sample size calculations, multilevel modeling, and structural equation modeling. This book also discusses measurement concepts that are crucial in psychometrics. For instance, the author explores how reliability and validity can be understood and evaluated using exploratory and confirmatory factor analysis. The author includes dozens of worked examples using real data to illustrate the theory and concepts.

Algorithmen werden von Grundprinzipien bis Implementierungsdetails genau besprochen Verstandlichkeit und Anschaulichkeit mit Schritt-fur-Schritt-Anleitungen Praxisorientierte und verstandlichen Darstellung der Arbeitsweise von Algorithmen Umfangreiche praktische Beispiele aus dem Ingenieuralltag

Dieses Buch bietet eine kurze und verstandliche Einfuhrung in das Softwarepaket MATHEMATICA und zeigt dessen Anwendung auf Problemstellungen aus der Ingenieurmathematik. Zunachst werden der Aufbau, die Arbeitsweise und die Moeglichkeiten von MATHEMATICA naher beschrieben. Anschliessend wird dieses Grundwissen auf die Grundlagen der Ingenieurmathematik, z.B. Matrizen, Differential- und Integralrechnung, angewendet. Der letzte Teil des Buches widmet sich den fortgeschrittenen Themen der Ingenieurmathematik. Dabei werden Differentialgleichungen, Transformationen, Optimierung, Wahrscheinlichkeitsrechnung und Statistik behandelt.Die Berechnungen werden jeweils ausfuhrlich dargestellt und an zahlreichen Beispielen illustriert.

In diesem Buch werden in kompakter Form mithilfe zahlreicher Beispiele die ublichen Modelle und Methoden der angewandten Wahrscheinlichkeitstheorie und Statistik dargestellt. Es ist daher insbesondere fur angehende Wirtschaftswissenschaftler, Ingenieure und Informatiker geeignet, welchen auch das didaktische Konzept des Buchs entgegenkommt: Verstandnisfragen und Aufgaben in Form von "Meilensteinen" erleichtern das eigenstandige UEberprufen des Lernfortschritts. Ein ausfuhrlicher mathematischer Anhang "Mathematik kompakt" stellt die wichtigsten Ergebnisse aus Analysis und linearer Algebra zum effizienten Nachschlagen zur Verfugung. Ein Glossar mit den wichtigsten englischen Begriffen sowie Tabellen der statistischen Testverteilungen runden die Darstellung ab.

With the development of computing technologies in today's modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.

Master MATLAB step-by-step

The MATLAB– "MATrix LABoratory"–computational environment offers a rich set of capabilities to efficiently solve a variety of complex analysis, simulation, and optimization problems. Flexible, powerful, and relatively easy to use, the MATLAB environment has become a standard cost-effective tool within the engineering, science, and technology communities. Excellent as a self-teaching guide for professionals as well as a textbook for students, Engineering and Scientific Computations Using MATLAB helps you fully understand the MATLAB environment, build your skills, and apply its features to a wide range of applications.

Going beyond traditional MATLAB user manuals and college texts, Engineering and Scientific Computations Using MATLAB guides you through the most important aspects and basics of MATLAB programming and problem-solving from fundamentals to practice. Augmenting its discussion with a wealth of practical worked-out examples and qualitative illustrations, this book demonstrates MATLAB’s capabilities and offers step-by-step instructions on how to apply the theory to a practical real-world problem. In particular, the book features:

• Coverage of a variety of complex physical and engineering systems described by nonlinear differential equations
• Detailed application of MATLAB to electromechanical systems
• MATLAB files, scripts, and statements, as well as SIMULINK models which can be easily modified for application-specific problems encountered in practice

Readable, user-friendly, and comprehensive in scope this is a welcome introduction to MATLAB for those new to the program and an ideal companion for engineers seeking in-depth mastery of the high-performance MATLAB environment.

This textbook explores two distinct stochastic processes that evolve at random: weakly stationary processes and discrete parameter Markov processes. Building from simple examples, the authors focus on developing context and intuition before formalizing the theory of each topic. This inviting approach illuminates the key ideas and computations in the proofs, forming an ideal basis for further study. After recapping the essentials from Fourier analysis, the book begins with an introduction to the spectral representation of a stationary process. Topics in ergodic theory follow, including Birkhoff's Ergodic Theorem and an introduction to dynamical systems. From here, the Markov property is assumed and the theory of discrete parameter Markov processes is explored on a general state space. Chapters cover a variety of topics, including birth-death chains, hitting probabilities and absorption, the representation of Markov processes as iterates of random maps, and large deviation theory for Markov processes. A chapter on geometric rates of convergence to equilibrium includes a splitting condition that captures the recurrence structure of certain iterated maps in a novel way. A selection of special topics concludes the book, including applications of large deviation theory, the FKG inequalities, coupling methods, and the Kalman filter. Featuring many short chapters and a modular design, this textbook offers an in-depth study of stationary and discrete-time Markov processes. Students and instructors alike will appreciate the accessible, example-driven approach and engaging exercises throughout. A single, graduate-level course in probability is assumed.

Statistische Verfahren werden sowohl in der Wirtschaft als auch in den Natur- und Sozialwissenschaften eingesetzt. Die Statistik gilt trotzdem als schwierig. Um diese Hemmschwelle zu uberwinden, geben die Autoren eine didaktisch ausgefeilte, anwendungsbezogene Einfuhrung in die Methoden der deskriptiven Statistik und Datenanalyse. Anhand praxisnaher Beispiele werden die Ideen des Datenmanagements und der Datenauswertung unter Einsatz von SPSS und R beschrieben. Viele Ubungsaufgaben (mit Losungen) unterstutzen das (Selbst-) Studium der Leser. Das Buch deckt den Stoff Statistik I an deutschsprachigen Universitaten vollstandig ab. Neu in dieser Auflage ist eine Einfuhrung in die logistische Regression, deren Konzept auch anhand der statistischen Software SPSS und R erlautert wird. Des Weiteren wurden viele Beispiele und Ubungsaufgaben thematisch uberarbeitet."

Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special. This book will teach you the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimising your code. By reading this book, you will learn: The difference between an object and its name, and why the distinction is important The important vector data structures, how they fit together, and how you can pull them apart using subsetting The fine details of functions and environments The condition system, which powers messages, warnings, and errors The powerful functional programming paradigm, which can replace many for loops The three most important OO systems: S3, S4, and R6 The tidy eval toolkit for metaprogramming, which allows you to manipulate code and control evaluation Effective debugging techniques that you can deploy, regardless of how your code is run How to find and remove performance bottlenecks The second edition is a comprehensive update: New foundational chapters: "Names and values," "Control flow," and "Conditions" comprehensive coverage of object oriented programming with chapters on S3, S4, R6, and how to choose between them Much deeper coverage of metaprogramming, including the new tidy evaluation framework use of new package like rlang (http://rlang.r-lib.org), which provides a clean interface to low-level operations, and purr (http://purrr.tidyverse.org/) for functional programming Use of color in code chunks and figures Hadley Wickham is Chief Scientist at RStudio, an Adjunct Professor at Stanford University and the University of Auckland, and a member of the R Foundation. He is the lead developer of the tidyverse, a collection of R packages, including ggplot2 and dplyr, designed to support data science. He is also the author of R for Data Science (with Garrett Grolemund), R Packages, and ggplot2: Elegant Graphics for Data Analysis.

in die Stochastik Mit Elementen der Bayes-Statistik und der Analyse unscharfer Information Dritte, uberarbeitete und erweiterte Auflage Springers Lehrbucher der Informatik Springer-Verlag Wien GmbH o. Univ.-Prof. Dipl.-Ing. Dr. techno Reinhard Kar Wolfgang Viertl Institut fur Statistik und Wahrscheinlichkeitstheorie Technische Universitat Wien, Osterreich Das Werk ist urheberrechtlich geschutzt. Die dadurch begrundeten Rechte, insbesondere die der Ubersetzung des Nachdruckes, der Entnahme von Abbildungen, der Funksendung, der Wiedergabe auf photomechani schem oder ahnlichem Wege und der Speicherung in Datenverarbeitungsanlagen, blei ben, auch bei nur auszugweiser Verwertung, vorbehalten. (c) 2003 Springer-Verlag Wien Ursprunglich erschienen bei Springer-Verlagl\Vien 2003 Produkthaftung: Samtliche Angaben in diesem Fachbuch/wissenschaftlichen Werk er folgen trotz sorgfaltiger Bearbeitung und Kontrolle ohne Gewahr. Eine Haftung des Autors oder des Verlages aus dem Inhalt dieses Werkes ist ausgeschlossen. Satz, Druck und Bindung: Druckerei zu Altenburg GmbH, D-04600 Altenburg Gedruckt auf saurefreiem, chlorfrei gebleichtem Papier - TCF SPIN: 10920640 Mit 51 Abbildungen Bibliografische Informationen Der Deutschen Bibliothek Die Deutsche Bibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; dataillierte bibliografische Daten sind im Internet uber abrufbar. ISSN 0938-9504 ISBN 978-3-211-00837-9 ISBN 978-3-7091-6080-0 (eBook) DOI 10.1007/978-3-7091-6080-0 Meiner Mutter in Dankbarkeitgewidmet Vorwort zur dritten Auflage Seit dem Erscheinen der zweiten Auflage haben sich einige Neuerungen ergeben, die in dieserAuflage beriicksichtigt sind. Da das Buch auch als Begleittext zu einer Vorlesung "Statistik und Wahrscheinlichkeitstheorie" dient, sind jetzt Uberlegungen zur statisti schen Beschreibung von Daten an den Beginn gestellt."

The Mata Book: A Book for Serious Programmers and Those Who Want to Be is the book that Stata programmers have been waiting for. Mata is a serious programming language for developing small- and large-scale projects and for adding features to Stata. What makes Mata serious is that it provides structures, classes, and pointers along with matrix capabilities. The book is serious in that it covers those advanced features, and teaches them. The reader is assumed to have programming experience, but only some programming experience. That experience could be with Stata's ado language, or with Python, Java, C++, Fortran, or other languages like them. As the book says, "being serious is a matter of attitude, not current skill level or knowledge". The author of the book is William Gould, who is also the designer and original programmer of Mata, of Stata, and who also happens to be the president of StataCorp.

This supplementary book for the social, behavioral, and health sciences helps readers with no prior knowledge of IBM (R) SPSS (R) Statistics, statistics, or mathematics learn the basics of SPSS. Designed to reduce fear and build confidence, the book guides readers through point-and-click sequences using clear examples from real scientific research and invites them to replicate the findings. Relevant outcomes are provided for reference, and exercises at the end of Chapters 2 - 5 provide additional practice. After reading the book and using the program, readers will come away with a basic knowledge of the most commonly used procedures in statistics.

Newly revised to specifically address Microsoft Excel 2019, this book is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical biological and life science problems. Excel is an effective learning tool for quantitative analyses in biological and life sciences courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. Excel 2019 for Biological and Life Sciences Statistics capitalizes on these improvements by teaching students and professionals how to apply Excel 2019 to statistical techniques necessary in their courses and work.Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand biological and life science problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full practice test (with answers in an appendix) that allows readers to test what they have learned. This new edition offers a wealth of new practice problems and solutions, as well as updated chapter content throughout.

Statistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming. The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining data sources, extracting parts of data sets, and reshaping data sets as needed for other analyses Generating general solutions using macros Customizing output Producing insight-inspiring data visualizations Parsing, processing, and analyzing text Programming solutions using matrices and connecting to R Processing text Programming with matrices Connecting SAS with R Covering topics that are part of both base and certification exams.

Aspects of environmental change are some of the greatest challenges faced by policymakers today. The key issues addressed by environmental science are often empirical, and in many instances very detailed, sizable datasets are available. Researchers in this field should have a solid understanding of the econometric tools best suited for analysis of these data. While complex and expensive physical models of the environment exist, it is becoming increasingly clear that reduced-form econometric models have an important role to play in modeling environmental phenomena. In short, successful environmental modeling does not necessarily require a structural model, but the econometric methods underlying a reduced-form approach must be competently executed. Environmental Econometrics Using Stata provides an important starting point for this journey by presenting a broad range of applied econometric techniques for environmental econometrics and illustrating how they can be applied in Stata. The emphasis is not only on how to formulate and fit models in Stata but also on the need to use a wide range of diagnostic tests in order to validate the results of estimation and subsequent policy conclusions. This focus on careful, reproducible research should be appreciated by academic and non-academic researchers who are seeking to produce credible, defensible conclusions about key issues in environmental science.

Learn from state-of-the-art examples in robotics, motors, detection filters, chemical processes, aircraft, and spacecraft. This is a practical reference for industry engineers using MATLAB to solve everyday problems. With MATLAB Recipes: A Problem-Solution Approach you will review contemporary MATLAB coding including the latest language features and use MATLAB as a software development environment including code organization, GUI development, and algorithm design and testing. This book provides practical guidance for using MATLAB to build a body of code you can turn to time and again for solving technical problems in your line of work. Develop algorithms, test them, visualize the results, and pass the code along to others to create a functional code base for your firm.

The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations--complete with sample code--of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining--including supervised and unsupervised data mining methods--are treated in detail, as are time series forecasting, text mining, and natural language processing. - Features a quick-learning format with concise tutorials and actionable analytics - Provides command-by-command translations of R to Python and vice versa - Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages - Offers numerous comparative examples and applications in both programming languages - Designed for use for practitioners and students that know one language and want to learn the other - Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.

This graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability. It provides a rigorous presentation of the core of mathematical statistics. Part I of this book constitutes a one-semester course on basic parametric mathematical statistics. Part II deals with the large sample theory of statistics - parametric and nonparametric, and its contents may be covered in one semester as well. Part III provides brief accounts of a number of topics of current interest for practitioners and other disciplines whose work involves statistical methods.

For courses in Political and Social Statistics Using the popular SPSS software to de-mystify statistics Using SPSS for Windows and Macintosh guides students through basic SPSS techniques, using step-by-step descriptions and explaining in detail how to avoid common pitfalls in the study of statistics. Authors Samuel Green and Neil Salkind provide extensive substantive information about each statistical technique, including a brief discussion of the technique, examples of how the statistic is applied, a sample data set that can be analyzed with the statistic, a discussion of the analysis results, practice exercises, and more. The Eighth Edition has been updated for SPSS version 23 (Windows/Mac), and now offers added accessibility and increased problem solving. NOTE: This ISBN is for a Pearson Books a la Carte edition: a convenient, three-hole-punched, loose-leaf text. In addition to the flexibility offered by this format, Books a la Carte editions offer students great value, as they cost significantly less than a bound textbook.

Marketing researchers, companies and business schools need to be able to use statistical procedures correctly and accurately interpret the outputs, yet generally these people are scared off by the statistics behind the different analyses procedures, thus they often rely on external sources to come up with profound answers to the proposed research questions. In an accessible and step by step approach, the authors show readers which procedures to use in which particular situation and how to practically execute them using IBM (R) SPSS Statistics. IBM (R) is one of the largest statistical software providers world-wide and their IBM (R) SPSS Statistics software offers a very user-friendly environment. The program uses a simple drag-and-drop menu interface, which is also suitable for non-experienced programmers. It is widely employed in companies and many business schools also use this software package. This straightforward, pragmatic reference manual will help: professional marketers who use statistical procedures in in IBM (R) SPSS Statistics; undergraduate and postgraduate students where marketing research and research methodology are taught; all researchers analyzing survey-based data in a wide range of frontier domains like psychology, finance, accountancy, negotiation, communication, sociology, criminology, management, information systems, etc. IBM (R)'s next-generation business analytic solutions help organizations of all sizes make sense of information in the context of their business. You can uncover insights more quickly and easily from all types of data-even big data-and on multiple platforms and devices. And, with self-service and built-in expertise and intelligence, you have the freedom and confidence to make smarter decisions that better address your business imperatives.

Essentials of Programming in Mathematica (R) provides an introduction suitable for readers with little or no background in the language as well as for those with some experience using programs such as C, Java, or Perl. The author, an established authority on Mathematica (R) programming, has written an example-driven text that covers the language from first principles, as well as including material from natural language processing, bioinformatics, graphs and networks, signal analysis, geometry, computer science, and many other applied areas. The book is appropriate for self-study or as a text for a course in programming in computational science. Readers will benefit from the author's tips, which provide insight and suggestions on small and large points. He also provides more than 350 exercises from novice through to advanced level with all of the solutions available online.

This text covers topics in algebraic geometry and commutative algebra with a strong perspective toward practical and computational aspects. The first four chapters form the core of the book. A comprehensive chart in the Preface illustrates a variety of ways to proceed with the material once these chapters are covered. In addition to the fundamentals of algebraic geometry-the elimination theorem, the extension theorem, the closure theorem and the Nullstellensatz-this new edition incorporates several substantial changes, all of which are listed in the Preface. The largest revision incorporates a new Chapter (ten), which presents some of the essentials of progress made over the last decades in computing Groebner bases. The book also includes current computer algebra material in Appendix C and updated independent projects (Appendix D). The book may serve as a first or second course in undergraduate abstract algebra and with some supplementation perhaps, for beginning graduate level courses in algebraic geometry or computational algebra. Prerequisites for the reader include linear algebra and a proof-oriented course. It is assumed that the reader has access to a computer algebra system. Appendix C describes features of Maple (TM), Mathematica (R) and Sage, as well as other systems that are most relevant to the text. Pseudocode is used in the text; Appendix B carefully describes the pseudocode used. Readers who are teaching from Ideals, Varieties, and Algorithms, or are studying the book on their own, may obtain a copy of the solutions manual by sending an email to [email protected]. From the reviews of previous editions: "...The book gives an introduction to Buchberger's algorithm with applications to syzygies, Hilbert polynomials, primary decompositions. There is an introduction to classical algebraic geometry with applications to the ideal membership problem, solving polynomial equations and elimination theory. ...The book is well-written. ...The reviewer is sure that it will be an excellent guide to introduce further undergraduates in the algorithmic aspect of commutative algebra and algebraic geometry." -Peter Schenzel, zbMATH, 2007 "I consider the book to be wonderful. ... The exposition is very clear, there are many helpful pictures and there are a great many instructive exercises, some quite challenging ... offers the heart and soul of modern commutative and algebraic geometry." -The American Mathematical Monthly

Learn how to use PTC(r) Mathcad Prime(r) 3.0, one of the world s leading tools for technical computing, in the context of engineering, science, and math applications. Quickly harness the power of PTC Mathcad Prime 3.0 to solve both simple and complex problems. "Essential PTC(r) Mathcad Prime(r) 3.0" is perfect for college students, first-time users, and experienced Mathcad 15 users who are moving to PTC Mathcad Prime 3.0.

Updated from Maxfield s popular "Essential Mathcad," this book introduces the most powerful functions and features of the new PTC Mathcad Prime 3.0 software and teaches how to apply them to create comprehensive calculations for any quantitative subject. Examples from several fields demonstrate the power and utility of PTC Mathcad s tools while also demonstrating how users can eff ectively incorporate Microsoft(r) Excel spreadsheets into the software.
Learn the basics faster: Chapter 1 introduces many fundamentals of Mathcad, allowing the reader to begin using the program in less time.Learn PTC Mathcad tools in context: Incorporates many applied examples and problems from a wide
variety of disciplines.Thorough discussion of many PTC Mathcad tools: Units, arrays, plotting, solving, symbolic calculations, programming, algebra, calculus, differential equations, reading from files, writing to files, and incorporating MS Excel spreadsheets.Each copy of the book includes a code to download PTC(r) Mathcad Prime(r) 3.0 Student Edition, giving
the user a full year of use from the time of installation. (The Student Edition software is intended for educational purposes only.)

The purpose of this book is to provide instruction and guidance on preparing quantitative data sets prior to answering a studyAEs research questions. Preparation may involve data management and manipulation tasks, data organization, structural changes to data files, or conducting preliminary analysis such as examining the scale of a variable, the validity of assumptions or the nature and extent of missing data. The oresultso from these essential first steps can also help guide a researcher in selecting the most appropriate statistical tests for his/her study. The book is intended to serve as a supplemental text in statistics or research courses offered in graduate programs in education, counseling, school psychology, behavioral sciences, and social sciences as well as undergraduate programs that contain a heavy emphasis on statistics. The content and issues covered are also beneficial for faculty and researchers who are knowledgeable about research design and able to use a statistical software package, but are unsure of the first steps to take with their data. Increasingly, faculty are forming partnerships with schools, clinics, and other institutions to help them analyze data in their extensive databases. This book can serve as a reference for helping them get existing data files in an appropriate form to run statistical analysis. This book is not a replacement for a statistics textbook. It assumes that readers have some knowledge of basic statistical concepts and use of statistical software, or that they will be learning these concepts and skills concurrently throughout the course. SPSS was chosen to illustrate the preparation, evaluation, and manipulation of data. However, students or researchers who do not use SPSS will benefit from the content since the overall structure and pedagogical approach of the book focuses heavily on the data issues and decisions to be made

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.