Featuring a timely presentation of total survey error (TSE), this edited volume introduces valuable tools for understanding and improving survey data quality in the context of evolving large-scale data sets This book provides an overview of the TSE framework and current TSE research as related to survey design, data collection, estimation, and analysis. It recognizes that survey data affects many public policy and business decisions and thus focuses on the framework for understanding and improving survey data quality. The book also addresses issues with data quality in official statistics and in social, opinion, and market research as these fields continue to evolve, leading to larger and messier data sets. This perspective challenges survey organizations to find ways to collect and process data more efficiently without sacrificing quality. The volume consists of the most up-to-date research and reporting from over 70 contributors representing the best academics and researchers from a range of fields. The chapters are broken out into five main sections: The Concept of TSE and the TSE Paradigm, Implications for Survey Design, Data Collection and Data Processing Applications, Evaluation and Improvement, and Estimation and Analysis. Each chapter introduces and examines multiple error sources, such as sampling error, measurement error, and nonresponse error, which often offer the greatest risks to data quality, while also encouraging readers not to lose sight of the less commonly studied error sources, such as coverage error, processing error, and specification error. The book also notes the relationships between errors and the ways in which efforts to reduce one type can increase another, resulting in an estimate with larger total error. This book: • Features various error sources, and the complex relationships between them, in 25 high-quality chapters on the most up-to-date research in the field of TSE • Provides comprehensive reviews of the literature on error sources as well as data collection approaches and estimation methods to reduce their effects • Presents examples of recent international events that demonstrate the effects of data error, the importance of survey data quality, and the real-world issues that arise from these errors • Spans the four pillars of the total survey error paradigm (design, data collection, evaluation and analysis) to address key data quality issues in official statistics and survey research Total Survey Error in Practice is a reference for survey researchers and data scientists in research areas that include social science, public opinion, public policy, and business. It can also be used as a textbook or supplementary material for a graduate-level course in survey research methods. Paul P. Biemer, PhD, is distinguished fellow at RTI International and associate director of Survey Research and Development at the Odum Institute, University of North Carolina, USA. Edith de Leeuw, PhD, is professor of survey methodology in the Department of Methodology and Statistics at Utrecht University, the Netherlands. Stephanie Eckman, PhD, is fellow at RTI International, USA. Brad Edwards is vice president, director of Field Services, and deputy area director at Westat, USA. Frauke Kreuter, PhD, is professor and director of the Joint Program in Survey Methodology, University of Maryland, USA; professor of statistics and methodology at the University of Mannheim, Germany; and head of the Statistical Methods Research Department at the Institute for Employment Research, Germany. Lars E. Lyberg, PhD, is senior advisor at Inizio, Sweden. N. Clyde Tucker, PhD, is principal survey methodologist at the American Institutes for Research, USA. Brady T. West, PhD, is research associate professor in the Survey Resea

Learning Kibana 5.0 is your gateway to mastering the art of data visualization using the powerful features of the Kibana platform. This book guides you through the process of creating stunning interactive dashboards and making data-driven insights accessible with real-time visualizations. Whether you're new to the Elastic stack or seeking to refine your expertise, this book equips you to harness Kibana's full potential. What this Book will help me do Build robust, real-time dashboards in Kibana to visualize complex datasets efficiently. Leverage Timelion to perform time-series data analysis and create metrics-based dashboards. Explore advanced analytics using the Graph plugin to uncover relationships and correlations in data. Learn how to create and deploy custom plugins to tailor Kibana to specific project needs. Understand how to use the Elastic stack to monitor, analyze, and optimize various types of data flows. Author(s) Bahaaldine Azarmi is a seasoned expert in the Elastic stack, known for his dedication to making complex technical topics approachable and practical. With years of experience in data analytics and software development, Bahaaldine shares not only his technical expertise but also his passion for helping professionals achieve their goals through clear, actionable guidance. His writing emphasizes hands-on learning and practical application. Who is it for? This book is perfect for developers, data visualization engineers, and data scientists who aim to hone their skills in data visualization and interactive dashboard development. It assumes a basic understanding of Elasticsearch and Logstash to maximize its practicality. If you aim to advance your career by learning how to optimize data architecture and solve real-world problems using the Elastic stack, this book is ideal for you.

A Panorama of Statistics: Perspectives, Puzzles and Paradoxes in Statistics Eric Sowey, School of Economics, The University of New South Wales, Sydney, Australia Peter Petocz, Department of Statistics, Macquarie University, Sydney, Australia This book is a stimulating panoramic tour – quite different from a textbook journey – of the world of statistics in both its theory and practice, for teachers, students and practitioners.At each stop on the tour, the authors investigate unusual and quirky aspects of statistics, highlighting historical, biographical and philosophical dimensions of this field of knowledge. Each chapter opens with perspectives on its theme, often from several points of view. Five original and thought-provoking questions follow. These aim at widening readers’ knowledge and deepening their insight. Scattered among the questions are entertaining puzzles to solve and tantalising paradoxes to explain. Readers can compare their own statistical discoveries with the authors’ detailed answers to all the questions. The writing is lively and inviting, the ideas are rewarding, and the material is extensively cross-referenced. A Panorama of Statistics: Leads readers to discover the fascinations of statistics. Is an enjoyable companion to an undergraduate statistics textbook. Is an enriching source of knowledge for statistics teachers and practitioners. Is unique among statistics books today for its memorable content and engaging style. Lending itself equally to reading through and to dipping into, A Panorama of Statistics will surprise teachers, students and practitioners by the variety of ways in which statistics can capture and hold their interest.

For one- and two-semester courses in introductory business statistics. Understand Business. Understand Data. The 3rd Edition of Statistics for Business: Decision Making and Analysis emphasizes an application-based approach, in which readers learn how to work with data to make decisions. In this contemporary presentation of business statistics, readers learn how to approach business decisions through a 4M Analytics decision making strategy—motivation, method, mechanics and message—to better understand how a business context motivates the statistical process and how the results inform a course of action. Each chapter includes hints on using Excel, Minitab Express, and JMP for calculations, pointing the reader in the right direction to get started with analysis of data. Also available with MyLab Statistics MyLab™ Statistics from Pearson is the world’s leading online resource for teaching and learning statistics; it integrates interactive homework, assessment, and media in a flexible, easy-to-use format. MyLab Statistics is a course management system that helps individual students succeed. It provides engaging experiences that personalize, stimulate, and measure learning for each student. Tools are embedded to make it easy to integrate statistical software into the course. Note: You are purchasing a standalone product; MyLab™does not come packaged with this content. Students, if interested in purchasing this title with MyLab, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase both the physical text and MyLab, search for: 0134763734 / 9780134763736 Statistics for Business: Decision Making and Analysis, Student Value Edition Plus MyLab Statistics with Pearson eText - Access Card Package, 3/e Package consists of: 0134497260 / 9780134497266 Statistics for Business: Decision Making and Analysis, Student Value Edition 0134748646 / 9780134748641 MyLab Statistics for Business Stats with Pearson eText - Standalone Access Card - for Statistics for Business: Decision Making and Analysis

Advanced Graph Theory focuses on some of the main notions arising in graph theory with an emphasis from the very start of the book on the possible applications of the theory and the fruitful links existing with linear algebra. The second part of the book covers basic material related to linear recurrence relations with application to counting and the asymptotic estimate of the rate of growth of a sequence satisfying a recurrence relation.

Leverage the power of visualization in business intelligence and data science to make quicker and better decisions. Use statistics and data mining to make compelling and interactive dashboards. This book will help those familiar with Tableau software chart their journey to being a visualization expert. Pro Tableau demonstrates the power of visual analytics and teaches you how to: Connect to various data sources such as spreadsheets, text files, relational databases (Microsoft SQL Server, MySQL, etc.), non-relational databases (NoSQL such as MongoDB, Cassandra), R data files, etc. Write your own custom SQL, etc. Perform statistical analysis in Tableau using R Use a multitude of charts (pie, bar, stacked bar, line, scatter plots, dual axis, histograms, heat maps, tree maps, highlight tables, box and whisker, etc.) What you'll learn Connect to various data sources such as relational databases (Microsoft SQL Server, MySQL), non-relational databases (NoSQL such as MongoDB, Cassandra), write your own custom SQL, join and blend data sources, etc. Leverage table calculations (moving average, year over year growth, LOD (Level of Detail), etc. Integrate Tableau with R Tell a compelling story with data by creating highly interactive dashboards Who this book is for All levels of IT professionals, from executives responsible for determining IT strategies to systems administrators, to data analysts, to decision makers responsible for driving strategic initiatives, etc. The book will help those familiar with Tableau software chart their journey to a visualization expert.

Data scientists are faced with the need to conduct continual experiments, particularly regarding user interface and product marketing. Designing experiments is a cornerstone of the practice of statistics, with clear application to data science. In this lesson, you’ll learn about A-B testing and hypothesis, or significance tests—critical aspects of experimental design for data science. What you’ll learn—and how you can apply it You will learn the central concepts of A-B testing, understand its role in designing and conducting data science experiments, and the characteristics of a proper A-B test. Through a series of sample tests, you’ll learn how to interpret results, and apply that insight to your analysis of the data. Since A-B tests are typically constructed with a hypothesis in mind, you’ll also learn how to conduct various hypothesis, or significance tests, enabling you to avoid misinterpreting randomness. This lesson is for you because You are a data scientist or analyst working with data, and want to gain beginner-level knowledge of key statistical concepts to improve the design, and outcome of your experimental tests with data. Prerequisites: Basic familiarity with coding in R Materials or downloads needed: n/a

Mastering Tableau is your comprehensive guide to becoming highly skilled in Tableau, focusing on advanced data visualization and practical applications. You will learn how to create complex dashboards, integrate R, and make the most of Tableau's features to deliver compelling insights. By the end of the book, you'll be ready to tackle real-world business intelligence challenges. What this Book will help me do Master advanced Tableau calculations such as row-level and aggregate-level calculations. Create engaging and efficient dashboards for professional data presentations. Integrate R functionalities with Tableau for predictive and advanced analytics. Design and implement custom geographic visualizations, including polygon maps. Optimize performance and best practices in Tableau for innovative BI solutions. Author(s) Jen Stirrup and None Baldwin are experienced data analysts and Tableau experts with years of practical experience in consulting and teaching. Jen has contributed significantly to the Tableau community through workshops and talks. Together, they provide structured guidance that helps readers master Tableau while emphasizing hands-on learning. Who is it for? This book is for business analysts aiming to enhance their data visualization skills using Tableau. Whether you are an intermediate Tableau user looking to tackle advanced techniques or someone wanting to streamline your BI workflows, this book focuses on practical problem-solving. It equips you to use Tableau effectively to create impactful visualizations and insights.

A non-technical guide to leveraging retail analytics for personal and competitive advantage Style & Statistics is a real-world guide to analytics in retail. Written specifically for the non-IT crowd, this book explains analytics in an approachable, understandable way, and provides examples of direct application to retail merchandise management, marketing, and operations. The discussion covers current industry trends and emerging-standard processes, and illustrates how analytics is providing new solutions to perennial retail problems. You'll learn how to leverage the benefits of analytics to boost your personal career, and how to interpret data in a way that's useful to the average end business user or shopper. Key concepts are detailed in easy-to-understand language, and numerous examples highlight the growing importance of understanding analytics in the retail environment. The power of analytics has become apparent across industries, but it's left an especially indelible mark on retail. It's a complex topic, but you don't need to be a data scientist to take advantage of the opportunities it brings. This book shows you what you need to know, and how to put analytics to work with retail-specific applications. Learn how analytics can help you be better at your job Dig deeper into the customer's needs, wants, and dreams Streamline merchandise management, pricing, marketing, and more Find solutions for inefficiencies and inaccuracies As the retail customer evolves, so must the retail industry. The retail landscape not only includes in-store but also website, mobile site, mobile apps, and social media . With more and more competition emerging on all sides, retailers need to use every tool at their disposal to create value and gain a competitive advantage. Analytics offers a number of ways to make your company stand out, whether it's through improved operations, customer experience, or any of the other myriad factors that build a great place to shop. Style & Statistics provides an analytics primer with a practical bent, specifically for the retail industry.

This book is for everyone who wants to make better forecasts. It is not about mathematics and statistics. It is about following a well-established forecasting process to create and implement good forecasts. This is true whether you are forecasting global markets, sales of SKUs, competitive strategy, or market disruptions. Today, most forecasts are generated using software. However, no amount of technology and statistics can compensate for a poor forecasting process. Forecasting is not just about generating a number. Forecasters need to understand the problems they are trying to solve. They also need to follow a process that is justifiable to other parties and be implemented in practice. This is what the book is about. Accurate forecasts are essential for predicting demand, identifying new market opportunities, forecasting risks, disruptions, innovation, competition, market growth and trends. Companies can navigate this daunting landscape and improve their forecasts by following some well-established principles. This book is written to provide the fundamentals business leaders need in order to make good forecasts. These fundamentals hold true regardless of what is being forecast and what technology is being used. It provides the basic foundational principles all companies need to achieve competitive forecast accuracy.

Real-world tips for creating business value Details on modeling, data clustering, and more Enterprise use cases to help you get started Learn to predict the future! Business today relies on effectively using data to predict trends and sales. Predictive analytics is the tool that can make it happen, and this book eliminates the tricks and shows you how to use it. You'll learn to prepare and process your data, create goals, build a predictive model, get your organization's stakeholders on board, and more. Inside... How to start a project Identifying data types Modeling tips Working with algorithms How data clustering works How data classification works How deep learning works Advice on presentations Step-by-step predictive modeling

Since this book first published in 2006, the field of information visualization has drastically. First, information visualization has exploded online and on other digital platforms. Second, information graphics reporting has encompassed nearly every sector of communications and business. This edition seeks to address these changes by providing learners with a cross-platform, cross-industry approach to instruction. It will include a robust, dynamic website complete with regularly updated examples of print, online and broadcast graphics, as well as useful tutorials and exercises.

Presents an introduction to differential equations, probability, and stochastic processes with real-world applications of queues with delay and delayed network queues Featuring recent advances in queueing theory and modeling, Delayed and Network Queues provides the most up-to-date theories in queueing model applications. Balancing both theoretical and practical applications of queueing theory, the book introduces queueing network models as tools to assist in the answering of questions on cost and performance that arise throughout the life of a computer system and signal processing. Written by well-known researchers in the field, the book presents key information for understanding the essential aspects of queues with delay and networks of queues with unreliable nodes and vacationing servers. Beginning with simple analytical fundamentals, the book contains a selection of realistic and advanced queueing models that address current deficiencies. In addition, the book presents the treatment of queues with delay and networks of queues, including possible breakdowns and disruptions that may cause delay. Delayed and Network Queues also features: Numerous examples and exercises with applications in various fields of study such as mathematical sciences, biomathematics, engineering, physics, business, health industry, and economics A wide array of practical applications of network queues and queueing systems, all of which are related to the appropriate stochastic processes Up-to-date topical coverage such as single- and multiserver queues with and without delays, along with the necessary fundamental coverage of probability and difference equations Discussions on queueing models such as single- and multiserver Markovian queues with balking, reneging, delay, feedback, splitting, and blocking, as well as their role in the treatment of networks of queues with and without delay and network reliability Delayed and Network Queues is an excellent textbook for upper-undergraduate and graduate-level courses in applied mathematics, queueing theory, queueing systems, probability, and stochastic processes. The book is also an ideal reference for academics and practitioners in mathematical sciences, biomathematics, operations research, management, engineering, physics, business, economics, health industry, and industrial engineering. Aliakbar Montazer Haghighi, PhD, is Professor and Head of the Department of Mathematics at Prairie View A&M University, USA, as well as founding Editor-in-Chief of Applications and Applied Mathematics: An International Journal (AAM). His research interests include probability, statistics, stochastic processes, and queueing theory. Among his research publications and books, Dr. Haghighi is the coauthor of Difference and Differential Equations with Applications in Queueing Theory (Wiley, 2013). Dimitar P. Mishev, PhD, is Professor in the Department of Mathematics at Prairie View A&M University, USA. His research interests include differential and difference equations and queueing theory. The author of numerous research papers and three books, Dr. Mishev is the coauthor of Difference and Differential Equations with Applications in Queueing Theory (Wiley, 2013).

In "Learning Tableau 10: Business Intelligence and data visualization that brings your business into focus", you will master data visualization and storytelling using Tableau 10. From foundational concepts to advanced features, this book will enable you to create compelling dashboards and conduct powerful data analysis, empowering businesses with actionable insights. What this Book will help me do Master the creation of effective and visually attractive dashboards in Tableau. Learn techniques for preparing and cleaning data for accurate visualizations. Build advanced visualizations that clarify and communicate complex ideas. Explore data clustering and distribution modeling to identify trends and make forecasts. Share your Tableau creations to promote a culture of evidence-based decision making. Author(s) The authors of 'Learning Tableau 10' are seasoned professionals with extensive experience in business intelligence and data visualization. They bring practical industry insights and a passion for empowering readers through clear instructional design. Their goal is to enable businesses to harness the full potential of Tableau for data-driven success. Who is it for? This book is ideal for data analysts, business professionals, or newcomers to data visualization who want to learn Tableau 10 from scratch or upgrade their skills. It is perfectly suited for beginners striving to bring professional insights and advanced users seeking to leverage Tableau's latest features effectively.

Practical Data Analysis provides a hands-on guide to mastering essential data analysis techniques using tools like Pandas, MongoDB, and Apache Spark. With step-by-step instructions, you'll explore how to process diverse data types, apply machine learning methods, and uncover actionable insights that can drive innovative projects and business solutions. What this Book will help me do Master data acquisition, formatting, and visualization techniques to prepare your data for analysis. Understand and apply machine learning algorithms for tasks like classification and forecasting. Learn to analyze textual data, such as performing sentiment analysis and text classification. Effectively work with databases using tools like MongoDB and handle big data with Apache Spark. Develop data-driven applications using real-world examples like image similarity searches and social network graph analysis. Author(s) None Cuesta and Dr. Sampath Kumar are experienced data scientists and educators. They have considerable experience applying data analysis techniques in various domains and a passion for teaching these skills. Their practical approach to data analysis ensures an engaging learning experience for readers. Who is it for? This book is ideal for developers and data enthusiasts aiming to incorporate practical data analysis into their projects. It is perfectly suited for readers with basic programming, statistics, and linear algebra knowledge. Even if you're new to professional data analysis, you'll find the step-by-step examples approachable. This book guides you in transforming raw data into valuable insights.

A thoroughly revised and updated edition of this introduction to modern statistical methods for shape analysis Shape analysis is an important tool in the many disciplines where objects are compared using geometrical features. Examples include comparing brain shape in schizophrenia; investigating protein molecules in bioinformatics; and describing growth of organisms in biology. This book is a significant update of the highly-regarded `Statistical Shape Analysis’ by the same authors. The new edition lays the foundations of landmark shape analysis, including geometrical concepts and statistical techniques, and extends to include analysis of curves, surfaces, images and other types of object data. Key definitions and concepts are discussed throughout, and the relative merits of different approaches are presented. The authors have included substantial new material on recent statistical developments and offer numerous examples throughout the text. Concepts are introduced in an accessible manner, while retaining sufficient detail for more specialist statisticians to appreciate the challenges and opportunities of this new field. Computer code has been included for instructional use, along with exercises to enable readers to implement the applications themselves in R and to follow the key ideas by hands-on analysis. Statistical Shape Analysis: with Applications in R will offer a valuable introduction to this fast-moving research area for statisticians and other applied scientists working in diverse areas, including archaeology, bioinformatics, biology, chemistry, computer science, medicine, morphometics and image analysis .

Nonparametric statistics provide a scientific methodology for cases where customary statistics are not applicable. Nonparametric statistics are used when the requirements for parametric analysis fail, such as when data are not normally distributed or the sample size is too small. The method provides an alternative for such cases and is often nearly as powerful as parametric statistics. Another advantage of nonparametric statistics is that it offers analytical methods that are not available otherwise. Nonparametric methods are intuitive and simple to comprehend, which helps researchers in the social sciences understand the methods in spite of lacking mathematical rigor needed in analytical methods customarily used in science. This book is a methodology book and bypasses theoretical proofs while providing comprehensive explanations of the logic behind the methods and ample examples, which are all solved using direct computations as well as by using Stata. It is arranged into two integrated volumes. Although each volume, and for that matter each chapter, can be used separately, it is advisable to read as much of both volumes as possible; because familiarity with what is applicable for different problems will enhance capabilities.

Nonparametric statistics provide a scientific methodology for cases where customary statistics are not applicable. Nonparametric statistics are used when the requirements for parametric analysis fail, such as when data are not normally distributed or the sample size is too small. The method provides an alternative for such cases and is often nearly as powerful as parametric statistics. Another advantage of nonparametric statistics is that it offers analytical methods that are not available otherwise. Nonparametric methods are intuitive and simple to comprehend, which helps researchers in the social sciences understand the methods in spite of lacking mathematical rigor needed in analytical methods customarily used in science. This book is a methodology book and bypasses theoretical proofs while providing comprehensive explanations of the logic behind the methods and ample examples, which are all solved using direct computations as well as by using Stata. It is arranged into two integrated volumes. Although each volume, and for that matter each chapter, can be used separately, it is advisable to read as much of both volumes as possible; because familiarity with what is applicable for different problems will enhance capabilities.

Most decisions and plans in a firm require a forecast. Not matching supply with demand can make or break any business, and that's why forecasting is so invaluable. Forecasting can appear as a frightening topic with many arcane equations to master. For this reason, the authors start out from the very basics and provide a non-technical overview of common forecasting techniques as well as organizational aspects of creating a robust forecasting process. The book also discusses how to measure forecast accuracy to hold people accountable and guide continuous improvement. This book does not require prior knowledge of higher mathematics, statistics, or operations research. It is designed to serve as a first introduction to the non-expert, such as a manager overseeing a forecasting group, or an MBA student who needs to be familiar with the broad outlines of forecasting without specializing in it.

Learn all of Excel's statistical tools Test your hypotheses and draw conclusions Use Excel to give meaning to your data Use Excel to interpret stats Statistical analysis with Excel is incredibly useful—and this book shows you that it can be easy, too! You'll discover how to use Excel's perfectly designed tools to analyze and understand data, predict trends, make decisions, and more. Tackle the technical aspects of Excel and start using them to interpret your data! Inside... Covers Excel 2016 for Windows® & Mac® users Check out new Excel stuff Make sense of worksheets Create shortcuts Tool around with analysis Use Quick Statistics Graph your data Work with probability Handle random variables

What is text mining, and how can it be used? What relevance do these methods have to everyday work in information science and the digital humanities? How does one develop competences in text mining? Working with Text provides a series of cross-disciplinary perspectives on text mining and its applications. As text mining raises legal and ethical issues, the legal background of text mining and the responsibilities of the engineer are discussed in this book. Chapters provide an introduction to the use of the popular GATE text mining package with data drawn from social media, the use of text mining to support semantic search, the development of an authority system to support content tagging, and recent techniques in automatic language evaluation. Focused studies describe text mining on historical texts, automated indexing using constrained vocabularies, and the use of natural language processing to explore the climate science literature. Interviews are included that offer a glimpse into the real-life experience of working within commercial and academic text mining. Introduces text analysis and text mining tools Provides a comprehensive overview of costs and benefits Introduces the topic, making it accessible to a general audience in a variety of fields, including examples from biology, chemistry, sociology, and criminology

Choose, manage, and present data Select the right forecasting method for your business Use moving averages and predict seasonal sales Create sales forecasts you can trust You don't need magic, luck, or an advanced math degree to develop reliable sales forecasts; you just need Excel and this book! This guide explains how forecasting works and how to use the tools built into Excel. You'll learn how to choose your data, set up tables, chart your baseline, to create both basic and advanced forecasts you can really use. Inside... Prevent common issues Why baselines matter How to organize your data Tips on setting up tables Working with pivot charts How to forecast seasonal sales revenue Forecasting with regression

Quantifying the User Experience: Practical Statistics for User Research, Second Edition, provides practitioners and researchers with the information they need to confidently quantify, qualify, and justify their data. The book presents a practical guide on how to use statistics to solve common quantitative problems that arise in user research. It addresses questions users face every day, including, Is the current product more usable than our competition? Can we be sure at least 70% of users can complete the task on their first attempt? How long will it take users to purchase products on the website? This book provides a foundation for statistical theories and the best practices needed to apply them. The authors draw on decades of statistical literature from human factors, industrial engineering, and psychology, as well as their own published research, providing both concrete solutions (Excel formulas and links to their own web-calculators), along with an engaging discussion on the statistical reasons why tests work and how to effectively communicate results. Throughout this new edition, users will find updates on standardized usability questionnaires, a new chapter on general linear modeling (correlation, regression, and analysis of variance), with updated examples and case studies throughout. Completely updated to provide practical guidance on solving usability testing problems with statistics for any project, including those using Six Sigma practices Includes new and revised information on standardized usability questionnaires Includes a completely new chapter introducing correlation, regression, and analysis of variance Shows practitioners which test to use, why they work, and best practices for application, along with easy-to-use Excel formulas and web-calculators for analyzing data Recommends ways for researchers and practitioners to communicate results to stakeholders in plain English

Statistics is a class that is required in many college majors, and it's an increasingly popular Advanced Placement high school course. In addition to math and technical students, many business and liberal arts students are required to take it as a fundamental component of their majors. A knowledge of statistical interpretation is vital for many careers. Idiot's Guides: Statistics explains the fundamental tenets in language anyone can understand. Content includes: - Calculating descriptive statistics - Measures of central tendency: mean, median, and mode - Probability - Variance analysis - Inferential statistics - Hypothesis testing - Organizing data into statistical charts and tables

Your indispensable guide to mastering the efficient use of D3.js in professional-standard data visualization projects. You will learn what data visualization is, how to work with it, and how to think like a D3.js expert, both practically and theoretically. Practical D3.js does not just show you how to use D3.js, it teaches you how to think like a data scientist and work with the data in the real world. In Part One, you will learn about theories behind data visualization. In Part Two, you will learn how to use D3.js to create the best charts and layouts. Uniquely, this book intertwines the technical details of D3.js with practical topics such as data journalism and the use of open government data. Written by leading data scientists Tarek Amr and Rayna Stamboliyska, this book is your guide to using D3.js in the real world -- add it to your library today. You Will Learn: How to think like a data scientist and present data in the best way What structure and design strategies you can use for compelling data visualization How to use data binding, animations and events, scales, and color pickers How to use shapes, path generators, arcs and polygons Who This Book is For: This book is for anyone who wants to learn to master the use of D3.js in a practical manner, while still learning the important theoretical aspects needed to enable them to work with their data in the best possible way.