Because of its large command structure and intricate syntax, Mathematica can be difficult to learn. Wolfram's Mathematica manual, while certainly comprehensive, is so large and complex that when trying to learn the software from scratch -- or find answers to specific questions -- one can be quickly overwhelmed. A Beginner's Guide to Mathematica offers a simple, step-by-step approach to help math-savvy newcomers build the skills needed to use the software in practice. Concise and easy to use, this book teaches by example and points out potential pitfalls along the way. The presentation starts with simple problems and discusses multiple solution paths, ranging from basic to elegant, to gradually introduce the Mathematica toolkit. More challenging and eventually cutting-edge problems follow. The authors place high value on notebook and file system organization, cross-platform capabilities, and data reading and writing. The text features an array of error messages you will likely encounter and clearly describes how to deal with those situations. While it is by no means exhaustive, this book offers a non-threatening introduction to Mathematica that will teach you the aspects needed for many practical applications, get you started on performing specific, relatively simple tasks, and enable you to build on this experience and move on to more real-world problems.

Economists are regularly confronted with results of quantitative economics research. Econometrics: Theory and Applications with EViews provides a broad introduction to quantitative economic methods, for example how models arise, their underlying assumptions and how estimates of parameters or other economic quantities are computed. The author combines econometric theory with practice by demonstrating its use with the software package EViews through extensive use of screen shots. The emphasis is on understanding how to select the right method of analysis for a given situation, and how to actually apply the theoretical methodology correctly. The EViews software package is available from 'Quantitive Micro Software'. Written for any undergraduate or postgraduate course in Econometrics.

"Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, "provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his or her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide. "

Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book aims to support a wide range of uses for the models by applied researchers in those and other fields by providing state-of-the-art descriptions of the implementation of LMMs in R. To help readers to get familiar with the features of the models and the details of carrying them out in R, the book includes a review of the most important theoretical concepts of the models. The presentation connects theory, software and applications. It is built up incrementally, starting with a summary of the concepts underlying simpler classes of linear models like the classical regression model, and carrying them forward to LMMs. A similar step-by-step approach is used to describe the R tools for LMMs. All the classes of linear models presented in the book are illustrated using real-life data. The book also introduces several novel R tools for LMMs, including new class of variance-covariance structure for random-effects, methods for influence diagnostics and for power calculations. They are included into an R package that should assist the readers in applying these and other methods presented in this text.

This Brief provides a roadmap for the R language and programming environment with signposts to further resources and documentation.

Until recently, acquiring a background in the basic methodological principles that apply to most types of investigations meant struggling to obtain results through laborious calculations. The advent of statistical software packages has removed much of the tedium and many of the errors of manual calculations and allowed a marked increase in the depth and sophistication of analyses. Although most statistics classes now incorporate some instruction in using a statistics package, most introductory texts do not. Quantitative Investigations in the Biosciences using MINITAB fills this void by providing an introduction to investigative methods that, in addition to outlining statistical principles and describing methods of calculations, also presents essential commands and interprets output from the statistics package MINITAB. The author introduces the three basic elements of investigations-design, analysis, and reporting-using an extremely accessible approach that keeps mathematical detail to a minimum. He groups statistical tests according to the type of problem they are used to examine, such as comparisons, sequential relationships, and associations. Quantitative Investigations in the Biosciences using MINITAB draws techniques and examples from a variety of subjects, ranging from physiology and biochemistry through to ecology, behavioral sciences, medicine, agriculture and horticulture, and complements the mathematical results with formal conclusions for all of the worked examples. It thus provides an ideal handbook for anyone in virtually any field who wants to apply statistical techniques to their investigations.

Written for the practicing electronics professional, Tolerance Analysis of Electronic Circuits Using MATHCADä offers a comprehensive, step-by-step treatment of methods used to perform analyses essential to the design process of circuit cards and systems of cards, including:
· worst-case analysis,
· limits for production testing,
· component stress analysis,
· determining if a design meets specification limits, and
· manufacturing yield analysis
Using a practical approach that allows engineers and technicians to put the techniques directly into practice, the author presents the mathematical procedures used to determine performance limits. The topics and techniques discussed include extreme value and root-sum-square analysis using symmetric and asymmetric tolerance, Monte Carlo analysis using normal and uniform distributions, sensitivity formulas, tolerance analyses of opamp offsets, and anomalies of high-Q ac circuits.

Written specifically for those with no prior programming experience and minimal quantitative training, this accessible text walks behavioral science students and researchers through the process of programming using MATLAB. The book explores examples, terms, and programming needs relevant to those in the behavioral sciences and helps readers perform virtually any computational function in solving their research problems. Principles are illustrated with usable code. Each chapter opens with a list of objectives followed by new commands required to accomplish those goals. These objectives also serve as a reference to help readers easily relocate a section of interest. Sample code and output and chapter problems demonstrate how to write a program and explore a model so readers can see the results obtained using different equations and values. A web site provides solutions to selected problems and the book's program code output and examples so readers can manipulate them as needed. The outputs on the website have color, motion, and sound. Highlights of the new edition include: *Updated to reflect changes in the most recent version of MATLAB, including special tricks and new functions. *More information on debugging and common errors and more basic problems in the rudiments of MATLAB to help novice users get up and running more quickly. *A new chapter on Psychtoolbox, a suite of programs specifically geared to behavioral science research. *A new chapter on Graphical User Interfaces (GUIs) for user-friendly communication. *Increased emphasis on pre-allocation of memory, recursion, handles, and matrix algebra operators. The book opens with an overview of what is to come and tips on how to write clear programs followed by pointers for interacting with MATLAB, including its commands and how to read error messages. The matrices chapter reviews how to store and access data. Chapter 4 examines how to carry out calculations followed by a review of how to perform various actions depending on the conditions. The chapter on input and output demonstrates how to design programs to create dialogs with users (e.g., participants in studies) and read and write data to and from external files. Chapter 7 reviews the data types available in MATLAB. Readers learn how to write a program as a stand-alone module in Chapter 8. In Chapters 9 and 10 readers learn how to create line and bar graphs or reshape images. Readers learn how to create animations and sounds in Chapter 11. The book concludes with tips on how to use MATLAB with applications such as GUIs and Psychtoolbox. Intended as a primary text for Matlab courses for advanced undergraduate and/or graduate students in experimental and cognitive psychology and/or neuroscience as well as a supplementary text for labs in data (statistical) analysis, research methods, and computational modeling (programming), the book also appeals to individual researchers in these disciplines who wish to get up and running in MATLAB.

A step-by-step approach to problem-solving techniques using SPSS(R) in the fields of sports science and physical education Featuring a clear and accessible approach to the methods, processes, and statistical techniques used in sports science and physical education, Sports Research with Analytical Solution using SPSS(R) emphasizes how to conduct and interpret a range of statistical analysis using SPSS. The book also addresses issues faced by research scholars in these fields by providing analytical solutions to various research problems without reliance on mathematical rigor. Logically arranged to cover both fundamental and advanced concepts, the book presents standard univariate and complex multivariate statistical techniques used in sports research such as multiple regression analysis, discriminant analysis, cluster analysis, and factor analysis. The author focuses on the treatment of various parametric and nonparametric statistical tests, which are shown through the techniques and interpretations of the SPSS outputs that are generated for each analysis. Sports Research with Analytical Solution using SPSS(R) also features: * Numerous examples and case studies to provide readers with practical applications of the analytical concepts and techniques * Plentiful screen shots throughout to help demonstrate the implementation of SPSS outputs * Illustrative studies with simulated realistic data to clarify the analytical techniques covered * End-of-chapter short answer questions, multiple choice questions, assignments, and practice exercises to help build a better understanding of the presented concepts * A companion website with associated SPSS data files and PowerPoint(R) presentations for each chapter Sports Research with Analytical Solution using SPSS(R) is an excellent textbook for upper-undergraduate, graduate, and PhD-level courses in research methods, kinesiology, sports science, medicine, nutrition, health education, and physical education. The book is also an ideal reference for researchers and professionals in the fields of sports research, sports science, physical education, and social sciences, as well as anyone interested in learning SPSS.

This book presents a theoretical and practical overview of computational modeling in bioengineering, focusing on a range of applications including electrical stimulation of neural and cardiac tissue, implantable drug delivery, cancer therapy, biomechanics, cardiovascular dynamics, as well as fluid-structure interaction for modelling of organs, tissues, cells and devices. It covers the basic principles of modeling and simulation with ordinary and partial differential equations using MATLAB and COMSOL Multiphysics numerical software. The target audience primarily comprises postgraduate students and researchers, but the book may also be beneficial for practitioners in the medical device industry.

Provides researchers with a reproducible research workflow for using R/RStudio to make the entire researchprocess reproducible; from data gathering, to analysis, to presentation Includes instructions not only for creating reproducible research in R, but also extensively discusses how to take advantage of recent developments in RStudio. Emphasizes the presentation of reproducible research with non-print formats such as HTML5 slideshows, blogs, and other web-based content. Covers a range of techniques to organize and remotely store files at all stages of the research process. These techniques both streamline the research process, especially by making revisions easier, and enhance The book itself will be reproducible, as all of the data, analysis, and markup files will be made available online.

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.

R is now the most widely used statistical package/language in university statistics departments and many research organisations. Its great advantages are that for many years it has been the leading-edge statistical package/language and that it can be freely downloaded from the R web site. Its cooperative development and open code also attracts many contributors meaning that the modelling and data analysis possibilities in R are much richer than in GLIM4, and so the R edition can be substantially more comprehensive than the GLIM4 edition. This text provides a comprehensive treatment of the theory of statistical modelling in R with an emphasis on applications to practical problems and an expanded discussion of statistical theory. A wide range of case studies is provided, using the normal, binomial, Poisson, multinomial, gamma, exponential and Weibull distributions, making this book ideal for graduates and research students in applied statistics and a wide range of quantitative disciplines.

The purpose of this handbook is to allow users to learn and master the mathematics software package MATLAB (R), as well as to serve as a quick reference to some of the most used instructions in the package. A unique feature of this handbook is that it can be used by the novice and by experienced users alike. For experienced users, it has four chapters with examples and applications in engineering, finance, physics, and optimization. Exercises are included, along with solutions available for the interested reader on the book's web page. These exercises are a complement for the interested reader who wishes to get a deeper understanding of MATLAB. Features Covers both MATLAB and introduction to Simulink Covers the use of GUIs in MATLAB and Simulink Offers downloadable examples and programs from the handbook's website Provides an introduction to object oriented programming using MATLAB Includes applications from many areas Includes the realization of executable files for MATLAB programs and Simulink models

This primer has been designed as a self-instructional text which serves to introduce the reader to both the principles of statistical modelling of covariance structures and to the use of the EQS software package. It is divided into three parts - the first covering the basic ideas and language of covariance structure modelling together with an introduction to the EQS package. The second section covers a wide variety of models suitable for cross-sectional and longitudinal data and the final section discusses a wide variety of practical problems. This book should be of interest to researchers in psychology, sociology and medicine who use the EQS software; applied and consultant statisticians.

This book is a valuable read for a diverse group of researchers and practitioners who analyze assessment data and construct test instruments. It focuses on the use of classical test theory (CTT) and item response theory (IRT), which are often required in the fields of psychology (e.g. for measuring psychological traits), health (e.g. for measuring the severity of disorders), and education (e.g. for measuring student performance), and makes these analytical tools accessible to a broader audience. Having taught assessment subjects to students from diverse backgrounds for a number of years, the three authors have a wealth of experience in presenting educational measurement topics, in-depth concepts and applications in an accessible format. As such, the book addresses the needs of readers who use CTT and IRT in their work but do not necessarily have an extensive mathematical background. The book also sheds light on common misconceptions in applying measurement models, and presents an integrated approach to different measurement methods, such as contrasting CTT with IRT and multidimensional IRT models with unidimensional IRT models. Wherever possible, comparisons between models are explicitly made. In addition, the book discusses concepts for test equating and differential item functioning, as well as Bayesian IRT models and plausible values using simple examples. This book can serve as a textbook for introductory courses on educational measurement, as supplementary reading for advanced courses, or as a valuable reference guide for researchers interested in analyzing student assessment data.

This volume, the third of a series, consists of applications of Mathematica (R) to a potpourri of more advanced topics. These include differential geometry of curves and surfaces, differential equations and special functions and complex analysis. Some of the newest features of Mathematica (R) are demonstrated and explained and some problems with the current implementation pointed out and possible future improvements suggested. Contains a large number of worked out examples. Explains some of the most recent mathematical features of Mathematica (R). Considers topics discussed rarely or not at all in the context of Mathematica (R). Can be used to supplement several different courses. Based on actual university courses.

Designed to help readers analyze and interpret research data using IBM SPSS, this user-friendly book shows readers how to choose the appropriate statistic based on the design; perform intermediate statistics, including multivariate statistics; interpret output; and write about the results. The book reviews research designs and how to assess the accuracy and reliability of data; how to determine whether data meet the assumptions of statistical tests; how to calculate and interpret effect sizes for intermediate statistics, including odds ratios for logistic and discriminant analyses; how to compute and interpret post-hoc power; and an overview of basic statistics for those who need a review. Unique chapters on multilevel linear modeling; multivariate analysis of variance (MANOVA); assessing reliability of data; multiple imputation; mediation, moderation, and canonical correlation; and factor analysis are provided. SPSS syntax with output is included for those who prefer this format.

The new edition features:

IBM SPSS version 22; although the book can be used with most older and newer versions

New discusiion of intraclass correlations (Ch. 3)

Expanded discussion of effect sizes that includes confidence intervals of effect sizes (ch.5)

New information on part and partial correlations and how they are interpreted and a new discussion on backward elimination, another useful multiple regression method (Ch. 6)

New chapter on how use a variable as a mediator or a moderator (ch. 7)

Revised chapter on multilevel and hierarchical linear modeling (ch. 12)

A new chapter (ch. 13) on multiple imputation that demonstrates how to deal with missing data

Updated web resources for instructors including PowerPoint slides, answers to interpretation questions, extra SPSS problems and for students, data sets, and chapter outlines and study guides. "

IBM SPSS for Intermediate Statistics, Fifth Edition "provides helpful teaching tools:

all of the key SPSS windows needed to perform the analyses

outputs with call-out boxes to highlight key points

interpretation sections and questions to help students better understand and interpret the output

extra problems with realistic data sets for practice using intermediate statistics

Appendices on how to get started with SPSS, write research questions, and basic statistics.

An ideal supplement for courses in either intermediate/advanced statistics or research methods taught in departments of psychology, education, and other social, behavioral, and health sciences. This book is also appreciated by researchers in these areas looking for a handy reference for SPSS"

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.

Many professional, high-quality surveys collect data on people's behaviour, experiences, lifestyles and attitudes. The data they produce is more accessible than ever before. This book provides students with a comprehensive introduction to using this data, as well as transactional data and big data sources, in their own research projects. Here you will find all you need to know about locating, accessing, preparing and analysing secondary data, along with step-by-step instructions for using IBM SPSS Statistics. You will learn how to: Create a robust research question and design that suits secondary analysis Locate, access and explore data online Understand data documentation Check and 'clean' secondary data Manage and analyse your data to produce meaningful results Replicate analyses of data in published articles and books Using case studies and video animations to illustrate each step of your research, this book provides you with the quantitative analysis skills you'll need to pass your course, complete your research project and compete in the job market. Exercises throughout the book and on the book's companion website give you an opportunity to practice, check your understanding and work hands on with real data as you're learning.

To write an accomplished program in the DATA step of SAS (R), programmers must understand programming logic and know how to implement and even create their own programming algorithm. Handbook of SAS (R) DATA Step Programming shows readers how best to manage and manipulate data by using the DATA step. The book helps novices avoid common mistakes resulting from a lack of understanding fundamental and unique SAS programming concepts. It explains that learning syntax does not solve all problems; rather, a thorough comprehension of SAS processing is needed for successful programming. The author also guides readers through a programming task. In most of the examples, the author first presents strategies and steps for solving the problem, then offers a solution, and finally gives a more detailed explanation of the solution. Understanding the DATA steps, particularly the program data vector (PDV), is critical to proper data manipulation and management in SAS. This book helps SAS programmers thoroughly grasp the concept of DATA step processing and write accurate programs in the DATA step. Numerous supporting materials, including data sets and programs used in the text, are available on the book's CRC Press web page.

While teaching the Numerical Methods for Engineers course over the last 15 years, the author found a need for a new textbook, one that was less elementary, provided applications and problems better suited for chemical engineers, and contained instruction in Visual Basic (R) for Applications (VBA). This led to six years of developing teaching notes that have been enhanced to create the current textbook, Numerical Methods for Chemical Engineers Using Excel (R), VBA, and MATLAB (R). Focusing on Excel gives the advantage of it being generally available, since it is present on every computer-PC and Mac-that has Microsoft Office installed. The VBA programming environment comes with Excel and greatly enhances the capabilities of Excel spreadsheets. While there is no perfect programming system, teaching this combination offers knowledge in a widely available program that is commonly used (Excel) as well as a popular academic software package (MATLAB). Chapters cover nonlinear equations, Visual Basic, linear algebra, ordinary differential equations, regression analysis, partial differential equations, and mathematical programming methods. Each chapter contains examples that show in detail how a particular numerical method or programming methodology can be implemented in Excel and/or VBA (or MATLAB in chapter 10). Most of the examples and problems presented in the text are related to chemical and biomolecular engineering and cover a broad range of application areas including thermodynamics, fluid flow, heat transfer, mass transfer, reaction kinetics, reactor design, process design, and process control. The chapters feature "Did You Know" boxes, used to remind readers of Excel features. They also contain end-of-chapter exercises, with solutions provided.

*When R creates copies, and how it affects memory usage and code performance *Everything you could ever want to know about functions *The differences between calling and exiting handlers *How to employ functional programming to solve modular tasks *The motivation, mechanics, usage, and limitations of R's highly pragmatic S3 OO system *The R6 OO system, which is more like OO programming in other languages *The rules that R uses to parse and evaluate expressions *How to use metaprogramming to generate HTML or LaTeX with elegant R code *How to identify and resolve performance bottlenecks