Michael Mitchell's A Visual Guide to Stata Graphics, Fourth Edition provides an essential introduction and reference for Stata graphics. The fourth edition retains the features that made the first three editions so useful: A complete guide to Stata's graph command Exhaustive examples of customized graphs Visual indexing of features-just look for a picture that matches what you want to do This edition includes new discussions of color, Unicode characters, export formats, sizing of graph elements, and schemes. The section on colors has been greatly expanded to include over 50 examples that demonstrate how to modify colors, add transparency, and change intensity. In the discussion of text modifications, Mitchell now shows how to include Unicode characters such as Greek letters, symbols, and emojis. New examples have also been added that show how to change the size of graph elements such as text, markers, and line widths using both absolute units (points, inches, and centimeters) as well as relative units (line large or *2 for two times the original size). Finally, the look of graphs throughout the book has changed-most graphs are now created using a common updated scheme. The book's visual style makes it easy to find exactly what you need. A color-coded, visual table of contents runs along the edge of every page and shows readers exactly where they are in the book. You can see the color-coded chapter tabs without opening the book, providing quick visual access to each chapter. The heart of each chapter is a series of entries that are typically formatted three to a page. Each entry shows a graph command (with the emphasized portion of the command highlighted in red), the resulting graph, a description of what is being done, and the dataset used. Because every feature, option, and edit is demonstrated with a graph, you can often flip through a section of the book to find exactly the effect you are seeking. The book begins with an introduction to Stata graphs that includes an overview of graphs types, schemes, and options and the process of building a graph. Then, it turns to detailed discussions of many graph types-scatterplots, regression fit plots, line plots, contour plots, bar graphs, box plots, and many others. Mitchell shows how to create each type of graph and how to use options to control the look of the graph. Because Stata's graph command will let you customize any aspect of the graph, Mitchell spends ample time showing you the most valuable options for obtaining the look you want. If you are in a hurry to discover one special option, you can skim the chapter until you see the effect you want and then glance at the command to see what is highlighted in red. After focusing on specific types of graphs, Mitchell undertakes an in-depth presentation of the options available across almost all graph types. This includes options that add and change the look of titles, notes, and such; control the number of ticks on axes; control the content and appearance of the numbers and labels on axes; control legends; add and change the look of annotations; graph over subgroups; change the look of markers and their labels; size graphs and their elements; and more. To complete the graphical journey, Mitchell discusses and demonstrates the 12 styles that unite and control the appearance of the myriad graph objects. These styles are angles, colors, clock positions, compass directions, connecting points, line patterns, line widths, margins, marker sizes, orientations, marker symbols, and text sizes. You won't want to overlook the appendix in this book. There Mitchell first gives a quick overview of the dozens of statistical graph commands that are not strictly the subject of the book. Even so, these commands use the graph command as an engine to draw their graphs; therefore, almost all that Mitchell has discussed applies to them. He also addresses combining graphs-showing you how to create complex and multipart images from previously created graphs. In a crucial section titled "Putting it all together", Mitchell shows us how to do just that. We learn more about overlaying twoway plots, and we learn how to combine data management and graphics to create plots such as bar charts of rates with capped confidence intervals. Mitchell concludes by warning us about mistakes that can be made when typing graph commands and how to correct them. The fourth edition of A Visual Guide to Stata Graphics is a complete guide to Stata's graph command and the associated Graph Editor. Whether you want to tame the Stata graph command, quickly find out how to produce a graphical effect, or learn approaches that can be used to construct custom graphs, this is the book to read.

This advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation. It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers understand how to develop new methodologies for small area estimation. It also includes numerous sample applications of small area estimation techniques. The underlying R code is provided in the text and applied to four datasets that mimic data from labor markets and living conditions surveys, where the socioeconomic indicators include the small area estimation of total unemployment, unemployment rates, average annual household incomes and poverty indicators. Given its scope, the book will be useful for master and PhD students, and for official and other applied statisticians.

An SPSS Companion for the Third Edition of The Fundamentals of Political Science Research offers students a chance to delve into the world of SPSS using real political science data sets and statistical analysis techniques directly from Paul M. Kellstedt and Guy D. Whitten's best-selling textbook. Built in parallel with the main text, this workbook teaches students to apply the techniques they learn in each chapter by reproducing the analyses and results from each lesson using SPSS. Students will also learn to create all of the tables and figures found in the textbook, leading to an even greater mastery of the core material. This accessible, informative, and engaging companion walks through the use of SPSS step-by-step, using command lines and screenshots to demonstrate proper use of the software. With the help of these guides, students will become comfortable creating, editing, and using data sets in SPSS to produce original statistical analyses for evaluating causal claims. End-of-chapter exercises encourage this innovation by asking students to formulate and evaluate their own hypotheses.

This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume is designed to guide the transition from classical to more advanced regression modeling, as well as to contribute to the rapid development of statistics and data science. With data and computing programs available to facilitate readers' learning experience, Statistical Regression Modeling promotes the applications of R in linear, nonlinear, longitudinal and multi-level regression. All included datasets, as well as the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This book will be valuable in graduate courses on applied regression, as well as for practitioners and researchers in the fields of data science, statistical analytics, public health, and related fields.

Chunyan Li is a course instructor with many years of experience in teaching about time series analysis. His book is essential for students and researchers in oceanography and other subjects in the Earth sciences, looking for a complete coverage of the theory and practice of time series data analysis using MATLAB. This textbook covers the topic's core theory in depth, and provides numerous instructional examples, many drawn directly from the author's own teaching experience, using data files, examples, and exercises. The book explores many concepts, including time; distance on Earth; wind, current, and wave data formats; finding a subset of ship-based data along planned or random transects; error propagation; Taylor series expansion for error estimates; the least squares method; base functions and linear independence of base functions; tidal harmonic analysis; Fourier series and the generalized Fourier transform; filtering techniques: sampling theorems: finite sampling effects; wavelet analysis; and EOF analysis.

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs. The book's main features are as follows: The content is written in an easy-to-follow and self-contained style. The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.

This book discusses the development of the Rosenbrock-Wanner methods from the origins of the idea to current research with the stable and efficient numerical solution and differential-algebraic systems of equations, still in focus. The reader gets a comprehensive insight into the classical methods as well as into the development and properties of novel W-methods, two-step and exponential Rosenbrock methods. In addition, descriptive applications from the fields of water and hydrogen network simulation and visual computing are presented.

R, an Open Source software, has become the "de facto" statistical computing environment. It has an excellent collection of data manipulation and graphics capabilities. It is extensible and comes with a large number of packages that allow statistical analysis at all levels - from simple to advanced - and in numerous fields including Medicine, Genetics, Biology, Environmental Sciences, Geology, Social Sciences and much more. The software is maintained and developed by academicians and professionals and as such, is continuously evolving and up to date. "Statistics and Data with R" presents an accessible guide to data manipulations, statistical analysis and graphics using R.

Assuming no previous knowledge of statistics or R, the book includes: A comprehensive introduction to the R language. An integrated approach to importing and preparing data for analysis, exploring and analyzing the data, and presenting results. Over 300 examples, including detailed explanations of the R scripts used throughout. Over 100 moderately large data sets from disciplines ranging from Biology, Ecology and Environmental Science to Medicine, Law, Military and Social Sciences. A parallel discussion of analyses with the normal density, proportions (binomial), counts (Poisson) and bootstrap methods. Two extensive indexes that include references to every R function (and its arguments and packages used in the book and to every introduced concept.

An accompanying Wiki website, http: //turtle.gis.umn.eduincludes all the scripts and data used in the book. The website also features a solutions manual, providing answers to all of the excercises presented in the book. Visitors are invited to download/upload data andscripts and share comments, suggestions and questions with other visitors. Students, researchers and practitioners will find this to be both a valuable learning resource in statistics and R and an excellent reference book.

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.

A report is only useful if those who receive it understand what it means. Knowing how to use Crystal Reports gives you the edge in producing reports from your database that really are crystal clear.

"Crystal Reports 2008 For Dummies" is a quick and easy guide to get you going with the latest version of this bestselling report-writing software. In fact, it's so popular that previous editions have made it a bestseller too. "Crystal Reports 2008 For Dummies" gives you just what you should know to produce the reports you'll need most often, including how to: Pull specific information from your database, sort and group it, and find the details you needUse dynamic or cascading promptsTroubleshoot and print reports and save time with templatesView reports on your LANWrite formulas to retrieve specific informationCreate and update OLAP reportsFormat reports, control page breaks, and even add graphics or Flash filesEnhance your reports with charts and mapsUse Crystal Reports in the enterprise

There's also a companion Web site with sample reports from the book and links to sites with more related information. With "Crystal Reports 2008 For Dummies" by your side, you'll soon be able to create reports from simple to spectacular, whenever the need arises.

This book presents the state of the art on numerical semigroups and related subjects, offering different perspectives on research in the field and including results and examples that are very difficult to find in a structured exposition elsewhere. The contents comprise the proceedings of the 2018 INdAM "International Meeting on Numerical Semigroups", held in Cortona, Italy. Talks at the meeting centered not only on traditional types of numerical semigroups, such as Arf or symmetric, and their usual properties, but also on related types of semigroups, such as affine, Puiseux, Weierstrass, and primary, and their applications in other branches of algebra, including semigroup rings, coding theory, star operations, and Hilbert functions. The papers in the book reflect the variety of the talks and derive from research areas including Semigroup Theory, Factorization Theory, Algebraic Geometry, Combinatorics, Commutative Algebra, Coding Theory, and Number Theory. The book is intended for researchers and students who want to learn about recent developments in the theory of numerical semigroups and its connections with other research fields.

Architect scalable, reliable, and maintainable applications for enterprises with Python Key Features Explore various Python design patterns used for enterprise software development Apply best practices for testing and performance optimization to build stable applications Learn about different attacking strategies used on enterprise applications and how to avoid them Book DescriptionDynamically typed languages like Python are continuously improving. With the addition of exciting new features and a wide selection of modern libraries and frameworks, Python has emerged as an ideal language for developing enterprise applications. Hands-On Enterprise Application Development with Python will show you how to build effective applications that are stable, secure, and easily scalable. The book is a detailed guide to building an end-to-end enterprise-grade application in Python. You will learn how to effectively implement Python features and design patterns that will positively impact your application lifecycle. The book also covers advanced concurrency techniques that will help you build a RESTful application with an optimized frontend. Given that security and stability are the foundation for an enterprise application, you'll be trained on effective testing, performance analysis, and security practices, and understand how to embed them in your codebase during the initial phase. You'll also be guided in how to move on from a monolithic architecture to one that is service oriented, leveraging microservices and serverless deployment techniques. By the end of the book, you will have become proficient at building efficient enterprise applications in Python. What you will learn Understand the purpose of design patterns and their impact on application lifecycle Build applications that can handle large amounts of data-intensive operations Uncover advanced concurrency techniques and discover how to handle a large number of requests in production Optimize frontends to improve the client-side experience of your application Effective testing and performance profiling techniques to detect issues in applications early in the development cycle Build applications with a focus on security Implement large applications as microservices to improve scalability Who this book is forIf you're a developer who wants to build enterprise-grade applications, this book is for you. Basic to intermediate-level of programming experience with Python and database systems is required to understand the concepts covered in this book.

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 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).

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.

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.

The Professional Product Owner's Guide to Maximizing Value with Scrum "This book presents a method of communicating our desires, cogently, coherently, and with a minimum of fuss and bother." -Ken Schwaber, Chairman & Founder, Scrum.org The role of the Product Owner is more crucial than ever. But it's about much more than mechanics: it's about taking accountability and refocusing on value as the primary objective of all you do. In The Professional Product Owner, two leading experts in successful Scrum product ownership show exactly how to do this. You'll learn how to identify where value can be found, measure it, and maximize it throughout your entire product lifecycle. Drawing on their combined 40+ years of experience in using agile and Scrum in product management, Don McGreal and Ralph Jocham guide you through all facets of envisioning, emerging, and maturing a product using the Scrum framework. McGreal and Jocham discuss strategy, showing how to connect Vision, Value, and Validation in ROI-focused agile product management. They lay out Scrum best-practices for managing complexity and continuously delivering value, and they define the concrete practices and tools you can use to manage Product Backlogs and release plans, all with the goal of making you a more successful Product Owner. Throughout, the authors share revealing personal experiences that illuminate obstacles to success and show how they can be overcome. Define success from the "outside in," using external customer-driven measurements to guide development and maximize value Bring empowerment and entrepreneurship to the Product Owner's role, and align everyone behind a shared business model Use Evidence-Based Management (EBMgt) to invest in the right places, make smarter decisions, and reduce risk Effectively apply Scrum's Product Owner role, artifacts, and events Populate and manage Product Backlogs, and use just-in-time specifications Plan and manage releases, improve transparency, and reduce technical debt Scale your product, not your Scrum Use Scrum to inject autonomy, mastery, and purpose into your product team's work Whatever your role in product management or agile development, this guide will help you deliver products that offer more value, more rapidly, and more often. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

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.