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

In honor of professor and renowned statistician R. Dennis Cook, this festschrift explores his influential contributions to an array of statistical disciplines ranging from experimental design and population genetics, to statistical diagnostics and all areas of regression-related inference and analysis. Since the early 1990s, Prof. Cook has led the development of dimension reduction methodology in three distinct but related regression contexts: envelopes, sufficient dimension reduction (SDR), and regression graphics. In particular, he has made fundamental and pioneering contributions to SDR, inventing or co-inventing many popular dimension reduction methods, such as sliced average variance estimation, the minimum discrepancy approach, model-free variable selection, and sufficient dimension reduction subspaces. A prolific researcher and mentor, Prof. Cook is known for his ability to identify research problems in statistics that are both challenging and important, as well as his deep appreciation for the applied side of statistics. This collection of Prof. Cook's collaborators, colleagues, friends, and former students reflects the broad array of his contributions to the research and instructional arenas of statistics.

SPSS syntax is the command language used by SPSS to carry out all of its commands and functions. In this book, Jacqueline Collier introduces the use of syntax to those who have not used it before, or who are taking their first steps in using syntax. Without requiring any knowledge of programming, the text outlines: - how to become familiar with the syntax commands; - how to create and manage the SPSS journal and syntax files; - and how to use them throughout the data entry, management and analysis process. Collier covers all aspects of data management from data entry through to data analysis, including managing the errors and the error messages created by SPSS. Syntax commands are clearly explained and the value of syntax is demonstrated through examples. This book also supports the use of SPSS syntax alongside the usual button and menu-driven graphical interface (GIF) using the two methods together, in a complementary way. The book is written in such a way as to enable you to pick and choose how much you rely on one method over the other, encouraging you to use them side-by-side, with a gradual increase in use of syntax as your knowledge, skills and confidence develop. This book is ideal for all those carrying out quantitative research in the health and social sciences who can benefit from SPSS syntax's capacity to save time, reduce errors and allow a data audit trail.

This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples. Regression Modelling Strategies presents full-scale case studies of non-trivial data-sets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression. As in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modelling techniques.

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.

This book chronicles a 10-year introduction of blended learning into the delivery at a leading technological university, with a longstanding tradition of technology-enabled teaching and learning, and state-of-the-art infrastructure. Hence, both teachers and students were familiar with the idea of online courses. Despite this, the longitudinal experiment did not proceed as expected. Though few technical problems, it required behavioural changes from teachers and learners, thus unearthing a host of socio-technical issues, challenges, and conundrums. With the undercurrent of design ideals such as "tech for good", any industrial sector must examine whether digital platforms are credible substitutes or at best complementary. In this era of Industry 4.0, higher education, like any other industry, should not be about the creative destruction of what we value in universities, but their digital transformation. The book concludes with an agenda for large, repeatable Randomised Controlled Trials (RCTs) to validate digital platforms that could fulfil the aspirations of the key stakeholder groups - students, faculty, and regulators as well as delving into the role of Massive Open Online Courses (MOOCs) as surrogates for "fees-free" higher education and whether the design of such a HiEd 4.0 platform is even a credible proposition. Specifically, the book examines the data-driven evidence within a design-based research methodology to present outcomes of two alternative instructional designs evaluated - traditional lecturing and blended learning. Based on the research findings and statistical analysis, it concludes that the inexorable shift to online delivery of education must be guided by informed educational management and innovation.

This book brings together some of the finest minds in the statistical and neural network research communities. It provides a broad overview of important current developments and highlights the likely future trends in the area of neural networks.

Inverse problems such as imaging or parameter identification deal with the recovery of unknown quantities from indirect observations, connected via a model describing the underlying context. While traditionally inverse problems are formulated and investigated in a static setting, we observe a significant increase of interest in time-dependence in a growing number of important applications over the last few years. Here, time-dependence affects a) the unknown function to be recovered and / or b) the observed data and / or c) the underlying process. Challenging applications in the field of imaging and parameter identification are techniques such as photoacoustic tomography, elastography, dynamic computerized or emission tomography, dynamic magnetic resonance imaging, super-resolution in image sequences and videos, health monitoring of elastic structures, optical flow problems or magnetic particle imaging to name only a few. Such problems demand for innovation concerning their mathematical description and analysis as well as computational approaches for their solution.

Group method of data handling (GMDH) is a typical inductive modeling method built on the principles of self-organization. Since its introduction, inductive modelling has been developed to support complex systems in prediction, clusterization, system identification, as well as data mining and knowledge extraction technologies in social science, science, engineering, and medicine.This is the first book to explore GMDH using MATLAB (matrix laboratory) language. Readers will learn how to implement GMDH in MATLAB as a method of dealing with big data analytics. Error-free source codes in MATLAB have been included in supplementary material (accessible online) to assist users in their understanding in GMDH and to make it easy for users to further develop variations of GMDH algorithms.

The investigation of the role of mechanical and mechano-chemical interactions in cellular processes and tissue development is a rapidly growing research field in the life sciences and in biomedical engineering. Quantitative understanding of this important area in the study of biological systems requires the development of adequate mathematical models for the simulation of the evolution of these systems in space and time. Since expertise in various fields is necessary, this calls for a multidisciplinary approach. This edited volume connects basic physical, biological, and physiological concepts to methods for the mathematical modeling of various materials by pursuing a multiscale approach, from subcellular to organ and system level. Written by active researchers, each chapter provides a detailed introduction to a given field, illustrates various approaches to creating models, and explores recent advances and future research perspectives. Topics covered include molecular dynamics simulations of lipid membranes, phenomenological continuum mechanics of tissue growth, and translational cardiovascular modeling. Modeling Biomaterials will be a valuable resource for both non-specialists and experienced researchers from various domains of science, such as applied mathematics, biophysics, computational physiology, and medicine.

This book shows how information theory, probability, statistics, mathematics and personal computers can be applied to the exploration of numbers and proportions in music. It brings the methods of scientific and quantitative thinking to questions like: What are the ways of encoding a message in music and how can we be sure of the correct decoding? How do claims of names hidden in the notes of a score stand up to scientific analysis? How many ways are there of obtaining proportions and are they due to chance? After thoroughly exploring the ways of encoding information in music, the ambiguities of numerical alphabets and the words to be found "hidden" in a score, the book presents a novel way of exploring the proportions in a composition with a purpose-built computer program and gives example results from the application of the techniques. These include information theory, combinatorics, probability, hypothesis testing, Monte Carlo simulation and Bayesian networks, presented in an easily understandable form including their development from ancient history through the life and times of J. S. Bach, making connections between science, philosophy, art, architecture, particle physics, calculating machines and artificial intelligence. For the practitioner the book points out the pitfalls of various psychological fallacies and biases and includes succinct points of guidance for anyone involved in this type of research. This book will be useful to anyone who intends to use a scientific approach to the humanities, particularly music, and will appeal to anyone who is interested in the intersection between the arts and science.With a foreword by Ruth Tatlow (Uppsala University), award winning author of Bach's Numbers: Compositional Proportion and Significance and Bach and the Riddle of the Number Alphabet."With this study Alan Shepherd opens a much-needed examination of the wide range of mathematical claims that have been made about J. S. Bach's music, offering both tools and methodological cautions with the potential to help clarify old problems." Daniel R. Melamed, Professor of Music in Musicology, Indiana University

The Distributed and Unified Numerics Environment (Dune) is a set of open-source C++ libraries for the implementation of finite element and finite volume methods. Over the last 15 years it has become one of the most commonly used libraries for the implementation of new, efficient simulation methods in science and engineering. Describing the main Dune libraries in detail, this book covers access to core features like grids, shape functions, and linear algebra, but also higher-level topics like function space bases and assemblers. It includes extensive information on programmer interfaces, together with a wealth of completed examples that illustrate how these interfaces are used in practice. After having read the book, readers will be prepared to write their own advanced finite element simulators, tapping the power of Dune to do so.

Review of the First Edition: The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc. who lack an advanced mathematics background. Unlike in many other textbooks, R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses, graphics, and simulations directly relating to meta-analysis... A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs. -Journal of Applied Statistics Statistical Meta-Analysis with R and Stata, Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions, which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions. What's New in the Second Edition: Adds Stata programs along with the R programs for meta-analysis Updates all the statistical meta-analyses with R/Stata programs Covers fixed-effects and random-effects MA, meta-regression, MA with rare-event, and MA-IPD vs MA-SS Adds five new chapters on multivariate MA, publication bias, missing data in MA, MA in evaluating diagnostic accuracy, and network MA Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health, medical research, governmental agencies, and the pharmaceutical industry.

Providing a detailed and pedagogical account of the rapidly-growing field of computational statistical physics, this book covers both the theoretical foundations of equilibrium and non-equilibrium statistical physics, and also modern, computational applications such as percolation, random walks, magnetic systems, machine learning dynamics, and spreading processes on complex networks. A detailed discussion of molecular dynamics simulations is also included, a topic of great importance in biophysics and physical chemistry. The accessible and self-contained approach adopted by the authors makes this book suitable for teaching courses at graduate level, and numerous worked examples and end of chapter problems allow students to test their progress and understanding.

Praise for the First Edition: ". . . the book serves as an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation." - Tzvetan Semerdjiev, Zentralblatt Math Computational statistics and statistical computing are two areas within statistics that may be broadly described as computational, graphical, and numerical approaches to solving statistical problems. Like its bestselling predecessor, Statistical Computing with R, Second Edition covers the traditional core material of these areas with an emphasis on using the R language via an examples-based approach. The new edition is up-to-date with the many advances that have been made in recent years. Features Provides an overview of computational statistics and an introduction to the R computing environment. Focuses on implementation rather than theory. Explores key topics in statistical computing including Monte Carlo methods in inference, bootstrap and jackknife, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation. Includes new sections, exercises and applications as well as new chapters on resampling methods and programming topics. Includes coverage of recent advances including R Studio, the tidyverse, knitr and ggplot2 Accompanied by online supplements available on GitHub including R code for all the exercises as well as tutorials and extended examples on selected topics. Suitable for an introductory course in computational statistics or for self-study, Statistical Computing with R, Second Edition provides a balanced, accessible introduction to computational statistics and statistical computing. About the Author Maria Rizzo is Professor in the Department of Mathematics and Statistics at Bowling Green State University in Bowling Green, Ohio, where she teaches statistics, actuarial science, computational statistics, statistical programming and data science. Prior to joining the faculty at BGSU in 2006, she was Assistant Professor in the Department of Mathematics at Ohio University in Athens, Ohio. Her main research area is energy statistics and distance correlation. She is the software developer and maintainer of the energy package for R. She also enjoys writing books including a forthcoming joint research monograph on energy statistics.

Starting from a basic knowledge of mathematics and mechanics gained in standard foundation classes, "Theory of Lift: Introductory Computational Aerodynamics in MATLAB/Octave" takes the reader conceptually through from the fundamental mechanics of lift to the stage of actually being able to make practical calculations and predictions of the coefficient of lift for realistic wing profile and planform geometries.

The classical framework and methods of aerodynamics are covered in detail and the reader is shown how they may be used to develop simple yet powerful MATLAB or Octave programs that accurately predict and visualise the dynamics of real wing shapes, using lumped vortex, panel, and vortex lattice methods.

This book contains all the mathematical development and formulae required in standard incompressible aerodynamics as well as dozens of small but complete working programs which can be put to use immediately using either the popular MATLAB or free Octave computional modelling packages.

Key features: Synthesizes the classical foundations of aerodynamics with hands-on computation, emphasizing interactivity and visualization.Includes complete source code for all programs, all listings having been tested for compatibility with both MATLAB and Octave.Companion website (www.wiley.com/go/mcbain) hosting codes and solutions.

"Theory of Lift: Introductory Computational Aerodynamics in MATLAB/Octave" is an introductory text for graduate and senior undergraduate students on aeronautical and aerospace engineering courses and also forms a valuable reference for engineers and designers.

Highlighting the latest advances in nonparametric and semiparametric statistics, this book gathers selected peer-reviewed contributions presented at the 4th Conference of the International Society for Nonparametric Statistics (ISNPS), held in Salerno, Italy, on June 11-15, 2018. It covers theory, methodology, applications and computational aspects, addressing topics such as nonparametric curve estimation, regression smoothing, models for time series and more generally dependent data, varying coefficient models, symmetry testing, robust estimation, and rank-based methods for factorial design. It also discusses nonparametric and permutation solutions for several different types of data, including ordinal data, spatial data, survival data and the joint modeling of both longitudinal and time-to-event data, permutation and resampling techniques, and practical applications of nonparametric statistics. The International Society for Nonparametric Statistics is a unique global organization, and its international conferences are intended to foster the exchange of ideas and the latest advances and trends among researchers from around the world and to develop and disseminate nonparametric statistics knowledge. The ISNPS 2018 conference in Salerno was organized with the support of the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and the University of Salerno.

.".".I know of no better book of its kind..." (Journal of the Royal Statistical Society, Vol 169 (1), January 2006)"

A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R

This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material like t--tests and chi--squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling.

Includes numerous worked examples and exercises within each chapter.

This book brings together two major trends: data science and blockchains. It is one of the first books to systematically cover the analytics aspects of blockchains, with the goal of linking traditional data mining research communities with novel data sources. Data science and big data technologies can be considered cornerstones of the data-driven digital transformation of organizations and society. The concept of blockchain is predicted to enable and spark transformation on par with that associated with the invention of the Internet. Cryptocurrencies are the first successful use case of highly distributed blockchains, like the world wide web was to the Internet. The book takes the reader through basic data exploration topics, proceeding systematically, method by method, through supervised and unsupervised learning approaches and information visualization techniques, all the way to understanding the blockchain data from the network science perspective. Chapters introduce the cryptocurrency blockchain data model and methods to explore it using structured query language, association rules, clustering, classification, visualization, and network science. Each chapter introduces basic concepts, presents examples with real cryptocurrency blockchain data and offers exercises and questions for further discussion. Such an approach intends to serve as a good starting point for undergraduate and graduate students to learn data science topics using cryptocurrency blockchain examples. It is also aimed at researchers and analysts who already possess good analytical and data skills, but who do not yet have the specific knowledge to tackle analytic questions about blockchain transactions. The readers improve their knowledge about the essential data science techniques in order to turn mere transactional information into social, economic, and business insights.

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.

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.

This thesis presents a revolutionary technique for modelling the dynamics of a quantum system that is strongly coupled to its immediate environment. This is a challenging but timely problem. In particular it is relevant for modelling decoherence in devices such as quantum information processors, and how quantum information moves between spatially separated parts of a quantum system. The key feature of this work is a novel way to represent the dynamics of general open quantum systems as tensor networks, a result which has connections with the Feynman operator calculus and process tensor approaches to quantum mechanics. The tensor network methodology developed here has proven to be extremely powerful: For many situations it may be the most efficient way of calculating open quantum dynamics. This work is abounds with new ideas and invention, and is likely to have a very significant impact on future generations of physicists.

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.

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.

This book collects peer-reviewed contributions on modern statistical methods and topics, stemming from the third workshop on Analytical Methods in Statistics, AMISTAT 2019, held in Liberec, Czech Republic, on September 16-19, 2019. Real-life problems demand statistical solutions, which in turn require new and profound mathematical methods. As such, the book is not only a collection of solved problems but also a source of new methods and their practical extensions. The authoritative contributions focus on analytical methods in statistics, asymptotics, estimation and Fisher information, robustness, stochastic models and inequalities, and other related fields; further, they address e.g. average autoregression quantiles, neural networks, weighted empirical minimum distance estimators, implied volatility surface estimation, the Grenander estimator, non-Gaussian component analysis, meta learning, and high-dimensional errors-in-variables models.