Genstat 5 Release 3 is a version of the statistical system developed by practising statisticians at Rothamsted Experimental Station. It provides statistical summary, analysis, data-handling, and graphics for interactive or batch users, and includes a customizable menu-based interface. Genstat is used worldwide on personal computers, workstations, and mainframe computers by statisticians, research workers, and students in all fields of application of statistics. Release 3 contains many new facilities: the analysis of ordered categorical data: generalized additive models; combination of information in multi-stratum experimental designs; extensions to the REML (residual maximum-likelihood) algorithm for testing fixed effects and to cater for correlation strucgures between random effects; estimation of paramenters of statistical distributions; further probability functions; simplified data input; and many more extensions, in high-resolution graphics, for calculations, and for manipulation. The manual has been rewritten for this release, including new chapters on Basic Statistics and REML, with extensive examples and illustrations. The text is suitable for users of Genstat 5 i.e. statis

This book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the theoretical aspects of the topic and offering hands-on help with the implementation. It discusses the pros and cons of various techniques and concepts, including multiple imputation quality diagnostics, an important topic for practitioners. It also presents current research and new, practically relevant developments in the field, and demonstrates the use of recent multiple imputation techniques designed for situations where distributional assumptions of the classical multiple imputation solutions are violated. In addition, the book features numerous practical tutorials for widely used R software packages to generate multiple imputations (norm, pan and mice). The provided R code and data sets allow readers to reproduce all the examples and enhance their understanding of the procedures. This book is intended for social and health scientists and other quantitative researchers who analyze incompletely observed data sets, as well as master's and PhD students with a sound basic knowledge of statistics.

Cohesively Incorporates Statistical Theory with R Implementation Since the publication of the popular first edition of this comprehensive textbook, the contributed R packages on CRAN have increased from around 1,000 to over 6,000. Designed for an intermediate undergraduate course, Probability and Statistics with R, Second Edition explores how some of these new packages make analysis easier and more intuitive as well as create more visually pleasing graphs. New to the Second Edition Improvements to existing examples, problems, concepts, data, and functions New examples and exercises that use the most modern functions Coverage probability of a confidence interval and model validation Highlighted R code for calculations and graph creation Gets Students Up to Date on Practical Statistical Topics Keeping pace with today's statistical landscape, this textbook expands your students' knowledge of the practice of statistics. It effectively links statistical concepts with R procedures, empowering students to solve a vast array of real statistical problems with R. Web Resources A supplementary website offers solutions to odd exercises and templates for homework assignments while the data sets and R functions are available on CRAN.

This book is written by testers for testers. In ten chapters, the authors provide answers to key questions in agile projects. They deal with cultural change processes for agile testing, with questions regarding the approach and organization of software testing, with the use of methods, techniques and tools, especially test automation, and with the redefined role of the tester in agile projects. The first chapter describes the cultural change brought about by agile development. In the second chapter, which addresses agile process models such as Scrum and Kanban, the authors focus on the role of quality assurance in agile development projects. The third chapter deals with the agile test organization and the positioning of testing in an agile team. Chapter 4 discusses the question of whether an agile tester should be a generalist or a specialist. In Chapter 5, the authors turn to the methods and techniques of agile testing, emphasizing the differences from traditional, phase-oriented testing. In Chapter 6, they describe which documents testers still need to create in an agile project. Next, Chapter 7 explains the efficient use of test automation, which is particularly important in agile development, as it is the main instrument for project acceleration and is necessary to support state-of-the-art DevOps approaches and Continuous Integration. Chapter 8 then adds examples from test tool practice extending test automation to include test management functionality. Chapter 9 is dedicated to training and its importance, emphasizing the role of employee training in getting started with agile development. Finally, Chapter 10 summarizes the results of the agile journey in general with a special focus on testing. To make the aspects described even more tangible, the specific topics of this book are accompanied by the description of experiences from concrete software development projects of various organizations. The examples demonstrate that different approaches can lead to solutions that meet the specific challenges of agile projects.

This book presents an introduction to structural equation modeling (SEM) and facilitates the access of students and researchers in various scientific fields to this powerful statistical tool. It offers a didactic initiation to SEM as well as to the open-source software, lavaan, and the rich and comprehensive technical features it offers. Structural Equation Modeling with lavaan thus helps the reader to gain autonomy in the use of SEM to test path models and dyadic models, perform confirmatory factor analyses and estimate more complex models such as general structural models with latent variables and latent growth models. SEM is approached both from the point of view of its process (i.e. the different stages of its use) and from the point of view of its product (i.e. the results it generates and their reading).

As explored in this open access book, higher education in STEM fields is influenced by many factors, including education research, government and school policies, financial considerations, technology limitations, and acceptance of innovations by faculty and students. In 2018, Drs. Ryoo and Winkelmann explored the opportunities, challenges, and future research initiatives of innovative learning environments (ILEs) in higher education STEM disciplines in their pioneering project: eXploring the Future of Innovative Learning Environments (X-FILEs). Workshop participants evaluated four main ILE categories: personalized and adaptive learning, multimodal learning formats, cross/extended reality (XR), and artificial intelligence (AI) and machine learning (ML). This open access book gathers the perspectives expressed during the X-FILEs workshop and its follow-up activities. It is designed to help inform education policy makers, researchers, developers, and practitioners about the adoption and implementation of ILEs in higher education.

The introduction of digital applications into businesses has revolutionized the way employees and managers carry out their jobs while also benefiting them socially. Smartphone and App Implementations that Improve Productivity looks at the benefits of apps in the workplace and introduces academic perspectives that link prospective advantages with practical commercial examples. The analysis is structured into chapters that include real world application while at the same time critically assess implied benefits of the new app technology and draw out the main findings and conclusions. Tahir M. Nisar brings into focus the emerging role of digital applications and big data in enterprise decision making. Readers will learn how companies can achieve more efficiency and effectiveness in their business operations through new types of organizational design strategies and mechanisms of employee mobility and work-life balance that draw on digital apps.

Text Mining with MATLAB (R) provides a comprehensive introduction to text mining using MATLAB. It is designed to help text mining practitioners, as well as those with little-to-no experience with text mining in general, familiarize themselves with MATLAB and its complex applications. The book is structured in three main parts: The first part, Fundamentals, introduces basic procedures and methods for manipulating and operating with text within the MATLAB programming environment. The second part of the book, Mathematical Models, is devoted to motivating, introducing, and explaining the two main paradigms of mathematical models most commonly used for representing text data: the statistical and the geometrical approach. Eventually, the third part of the book, Techniques and Applications, addresses general problems in text mining and natural language processing applications such as document categorization, document search, content analysis, summarization, question answering, and conversational systems. This second edition includes updates in line with the recently released "Text Analytics Toolbox" within the MATLAB product and introduces three new chapters and six new sections in existing ones. All descriptions presented are supported with practical examples that are fully reproducible. Further reading, as well as additional exercises and projects, are proposed at the end of each chapter for those readers interested in conducting further experimentation.

This text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank. Features: * An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. * Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc. * Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning. New edition highlights: * Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering * Restructured to make the logics more straightforward and sections self-contained Core Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners.

Recent data shows that 87% of Artificial Intelligence/Big Data projects don't make it into production (VB Staff, 2019), meaning that most projects are never deployed. This book addresses five common pitfalls that prevent projects from reaching deployment and provides tools and methods to avoid those pitfalls. Along the way, stories from actual experience in building and deploying data science projects are shared to illustrate the methods and tools. While the book is primarily for data science practitioners, information for managers of data science practitioners is included in the Tips for Managers sections.

This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.

Gain an overview of the core principles of IS and how it is practiced today as the concise, nine-chapter FUNDAMENTALS OF INFORMATION SYSTEMS, 9th edition combines the latest research with the most current coverage available. The book addresses analytics; big data; business intelligence; cloud computing; cybercrime and information system security; e-commerce; enterprise systems; ethical, legal, and social issues of information systems; mobile computing solutions; and systems acquisition and development. You learn how information systems can increase profits and reduce costs as you explore the latest information on artificial intelligence, change management, consumerization of information systems, data governance, energy and environmental concerns, global IS challenges, Internet of Everything, Internet censorship and net neutrality, IS careers, and virtual teams. Leading online MindTap help you further maximize success as an employee, decision maker, and business leader.

This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics. The coverage is comprehensive, ranging from the underlying theory to computational implementation, methodology, and diverse applications in various areas of science. This is achieved by describing SMC algorithms as particular cases of a general framework, which involves concepts such as Feynman-Kac distributions, and tools such as importance sampling and resampling. This general framework is used consistently throughout the book. Extensive coverage is provided on sequential learning (filtering, smoothing) of state-space (hidden Markov) models, as this remains an important application of SMC methods. More recent applications, such as parameter estimation of these models (through e.g. particle Markov chain Monte Carlo techniques) and the simulation of challenging probability distributions (in e.g. Bayesian inference or rare-event problems), are also discussed. The book may be used either as a graduate text on Sequential Monte Carlo methods and state-space modeling, or as a general reference work on the area. Each chapter includes a set of exercises for self-study, a comprehensive bibliography, and a "Python corner," which discusses the practical implementation of the methods covered. In addition, the book comes with an open source Python library, which implements all the algorithms described in the book, and contains all the programs that were used to perform the numerical experiments.

The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What's New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R's repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.

This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples. As such, it quickly introduces readers to the peculiarities of each subject from both theoretical and the practical points of view. It also includes numerous examples and real-world applications that demonstrate how to handle different types of time series data. The associated software package, SSMMATLAB, is written in MATLAB and also runs on the free OCTAVE platform. The book focuses on linear time series models using a state space approach, with the Kalman filter and smoother as the main tools for model estimation, prediction and signal extraction. A chapter on state space models describes these tools and provides examples of their use with general state space models. Other topics discussed in the book include ARIMA; and transfer function and structural models; as well as signal extraction using the canonical decomposition in the univariate case, and VAR, VARMA, cointegrated VARMA, VARX, VARMAX, and multivariate structural models in the multivariate case. It also addresses spectral analysis, the use of fixed filters in a model-based approach, and automatic model identification procedures for ARIMA and transfer function models in the presence of outliers, interventions, complex seasonal patterns and other effects like Easter, trading day, etc. This book is intended for both students and researchers in various fields dealing with time series. The software provides numerous automatic procedures to handle common practical situations, but at the same time, readers with programming skills can write their own programs to deal with specific problems. Although the theoretical introduction to each topic is kept to a minimum, readers can consult the companion book 'Multivariate Time Series With Linear State Space Structure', by the same author, if they require more details.

This book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved. Signal processing can broadly be considered to be the recovery of information from physical observations. The received signals are usually disturbed by thermal, electrical, atmospheric or intentional interferences, and due to their random nature, statistical techniques play an important role in their analysis. Statistics is also used in the formulation of appropriate models to describe the behavior of systems, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Analyzing different real-world data sets to illustrate how different models can be used in practice, and highlighting open problems for future research, the book is a valuable resource for senior undergraduate and graduate students specializing in mathematics or statistics.

Testing for economic convergence across countries has been a central issue in the literature of economic growth and development. This book introduces a modern framework to study the cross-country convergence dynamics in labor productivity and its proximate sources: capital accumulation and aggregate efficiency. In particular, recent convergence dynamics of developed as well as developing countries are evaluated through the lens of a non-linear dynamic factor model and a clustering algorithm for panel data. This framework allows us to examine key economic phenomena such as technological heterogeneity and multiple equilibria. In this context, the book provides a succinct review of the recent club convergence literature, a comparative view of developed and developing countries, and a tutorial on how to implement the club convergence framework in the statistical software Stata.

YOU HAVE TO OWN THIS BOOK! Software Exorcism: A Handbook for Debugging and Optimizing Legacy Code takes an unflinching, no bulls$&# look at behavioral problems in the software engineering industry, shedding much-needed light on the social forces that make it difficult for programmers to do their job. Do you have a co-worker who perpetually writes bad code that you are forced to clean up? This is your book. While there are plenty of books on the market that cover debugging and short-term workarounds for bad code, Reverend Bill Blunden takes a revolutionary step beyond them by bringing our attention to the underlying illnesses that plague the software industry as a whole. Further, Software Exorcism discusses tools and techniques for effective and aggressive debugging, gives optimization strategies that appeal to all levels of programmers, and presents in-depth treatments of technical issues with honest assessments that are not biased toward proprietary solutions.

This Springer brief addresses the challenges encountered in the study of the optimization of time-nonhomogeneous Markov chains. It develops new insights and new methodologies for systems in which concepts such as stationarity, ergodicity, periodicity and connectivity do not apply. This brief introduces the novel concept of confluencity and applies a relative optimization approach. It develops a comprehensive theory for optimization of the long-run average of time-nonhomogeneous Markov chains. The book shows that confluencity is the most fundamental concept in optimization, and that relative optimization is more suitable for treating the systems under consideration than standard ideas of dynamic programming. Using confluencity and relative optimization, the author classifies states as confluent or branching and shows how the under-selectivity issue of the long-run average can be easily addressed, multi-class optimization implemented, and Nth biases and Blackwell optimality conditions derived. These results are presented in a book for the first time and so may enhance the understanding of optimization and motivate new research ideas in the area.

Nonlinear Parameter Optimization Using RJohn C. Nash, Telfer School of Management, University of Ottawa, Canada

A systematic and comprehensive treatment of optimization software using RIn recent decades, optimization techniques have been streamlined by computational and artificial intelligence methods to analyze more variables, especially under non-linear, multivariable conditions, more quickly than ever before.Optimization is an important tool for decision science and for the analysis of physical systems used in engineering. Nonlinear Parameter Optimization with R explores the principal tools available in R for function minimization, optimization, and nonlinear parameter determination and features numerous examples throughout.

Nonlinear Parameter Optimization with R: - Provides a comprehensive treatment of optimization techniques- Examines optimization problems that arise in statistics and how to solve them using R- Enables researchers and practitioners to solve parameter determination problems- Presents traditional methods as well as recent developments in R- Is supported by an accompanying website featuring R code, examples and datasets

Researchers and practitioners who have to solve parameter determination problems who are users of R but are novices in the field optimization or function minimization will benefit from this book. It will also be useful for scientists building and estimating nonlinear models in various fields such as hydrology, sports forecasting, ecology, chemical engineering, pharmaco-kinetics, agriculture, economics and statistics.

The objective of this text is to introduce RStudio to practitioners and students and enable them to use R in their everyday work. It is not a statistical textbook, the purpose is to transmit the joy of analyzing data with RStudio. Practitioners and students learn how RStudio can be installed and used, they learn to import data, write scripts and save working results. Furthermore, they learn to employ descriptive statistics and create graphics with RStudio. Additionally, it is shown how RStudio can be used to test hypotheses, run an analysis of variance and regressions. To deepen the learned content, tasks are included with the solutions provided at the end of the textbook. This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.

Now in its second edition, Text Analysis with R provides a practical introduction to computational text analysis using the open source programming language R. R is an extremely popular programming language, used throughout the sciences; due to its accessibility, R is now used increasingly in other research areas. In this volume, readers immediately begin working with text, and each chapter examines a new technique or process, allowing readers to obtain a broad exposure to core R procedures and a fundamental understanding of the possibilities of computational text analysis at both the micro and the macro scale. Each chapter builds on its predecessor as readers move from small scale "microanalysis" of single texts to large scale "macroanalysis" of text corpora, and each concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book's focus is on making the technical palatable and making the technical useful and immediately gratifying. Text Analysis with R is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological toolkit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that readers simply cannot gather using traditional qualitative methods of close reading and human synthesis. This new edition features two new chapters: one that introduces dplyr and tidyr in the context of parsing and analyzing dramatic texts to extract speaker and receiver data, and one on sentiment analysis using the syuzhet package. It is also filled with updated material in every chapter to integrate new developments in the field, current practices in R style, and the use of more efficient algorithms.

Program for data analysis using R and learn practical skills to make your work more efficient. This revised book explores how to automate running code and the creation of reports to share your results, as well as writing functions and packages. It includes key R 4 features such as a new color palette for charts, an enhanced reference counting system, and normalization of matrix and array types where matrix objects now formally inherit from the array class, eliminating inconsistencies. Advanced R 4 Data Programming and the Cloud is not designed to teach advanced R programming nor to teach the theory behind statistical procedures. Rather, it is designed to be a practical guide moving beyond merely using R; it shows you how to program in R to automate tasks. This book will teach you how to manipulate data in modern R structures and includes connecting R to databases such as PostgreSQL, cloud services such as Amazon Web Services (AWS), and digital dashboards such as Shiny. Each chapter also includes a detailed bibliography with references to research articles and other resources that cover relevant conceptual and theoretical topics. What You Will Learn Write and document R functions using R 4 Make an R package and share it via GitHub or privately Add tests to R code to ensure it works as intended Use R to talk directly to databases and do complex data management Run R in the Amazon cloud Deploy a Shiny digital dashboard Generate presentation-ready tables and reports using R Who This Book Is For Working professionals, researchers, and students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to take their R coding and programming to the next level.

This book presents strategies for analyzing qualitative and mixed methods data with MAXQDA software, and provides guidance on implementing a variety of research methods and approaches, e.g. grounded theory, discourse analysis and qualitative content analysis, using the software. In addition, it explains specific topics, such as transcription, building a coding frame, visualization, analysis of videos, concept maps, group comparisons and the creation of literature reviews. The book is intended for masters and PhD students as well as researchers and practitioners dealing with qualitative data in various disciplines, including the educational and social sciences, psychology, public health, business or economics.

This book illustrates the role of software architecture and its application in business. The author describes enterprise architecture along with business architecture to show the role of software architecture in both areas. The place of software architecture in business is outlined from many perspectives in this context. The book outlines quality attributes and how managers can use software architecture to build high quality products. Topics include business software architecture, dealing with qualities, achieving quality attributes, managing business qualities, software product line, Internet of Things (IOT), and Service Oriented Business Architecture. The book is intended to benefit students, researchers, software architects, and business architects. Provides quick and easy access to all the important aspects of software architecture in business; Highlights a wide variety of concepts of software architecture in a straightforward manner, for students, practitioners, or architects; Presents different applications of software architecture in business.