Presents statistical methodologies for analyzing common types of data from method comparison experiments and illustrates their applications through detailed case studies Measuring Agreement: Models, Methods, and Applications features statistical evaluation of agreement between two or more methods of measurement of a variable with a primary focus on continuous data. The authors view the analysis of method comparison data as a two-step procedure where an adequate model for the data is found, and then inferential techniques are applied for appropriate functions of parameters of the model. The presentation is accessible to a wide audience and provides the necessary technical details and references. In addition, the authors present chapter-length explorations of data from paired measurements designs, repeated measurements designs, and multiple methods; data with covariates; and heteroscedastic, longitudinal, and categorical data. The book also: • Strikes a balance between theory and applications • Presents parametric as well as nonparametric methodologies • Provides a concise introduction to Cohen’s kappa coefficient and other measures of agreement for binary and categorical data • Discusses sample size determination for trials on measuring agreement • Contains real-world case studies and exercises throughout • Provides a supplemental website containing the related datasets and R code Measuring Agreement: Models, Methods, and Applications is a resource for statisticians and biostatisticians engaged in data analysis, consultancy, and methodological research. It is a reference for clinical chemists, ecologists, and biomedical and other scientists who deal with development and validation of measurement methods. This book can also serve as a graduate-level text for students in statistics and biostatistics.

Provides a one-stop resource for engineers learning biostatistics using MATLAB® and WinBUGS Through its scope and depth of coverage, this book addresses the needs of the vibrant and rapidly growing bio-oriented engineering fields while implementing software packages that are familiar to engineers. The book is heavily oriented to computation and hands-on approaches so readers understand each step of the programming. Another dimension of this book is in parallel coverage of both Bayesian and frequentist approaches to statistical inference. It avoids taking sides on the classical vs. Bayesian paradigms, and many examples in this book are solved using both methods. The results are then compared and commented upon. Readers have the choice of MATLAB® for classical data analysis and WinBUGS/OpenBUGS for Bayesian data analysis. Every chapter starts with a box highlighting what is covered in that chapter and ends with exercises, a list of software scripts, datasets, and references. Engineering Biostatistics: An Introduction using MATLAB® and WinBUGS also includes: parallel coverage of classical and Bayesian approaches, where appropriate substantial coverage of Bayesian approaches to statistical inference material that has been classroom-tested in an introductory statistics course in bioengineering over several years exercises at the end of each chapter and an accompanying website with full solutions and hints to some exercises, as well as additional materials and examples Engineering Biostatistics: An Introduction using MATLAB® and WinBUGS can serve as a textbook for introductory-to-intermediate applied statistics courses, as well as a useful reference for engineers interested in biostatistical approaches.

This book offers a standardized approach for research aspirants working in the various areas. At the same time, all the major topics in social research have also been detailed thoroughly which makes this book a very good frame of study for students and researchers in diverse fields. This book charts new and evolving terrain of social research by covering qualitative, quantitative and mixed approach. The chapters has extensive number of case studies that help researchers to understand practical implications of the research and includes plenty of diagrammatic representations for easy understanding of various theories and procedures. Each phase of research is explained in detail so that even beginners can also effectively utilize this book. It is written in a highly interactive manner, which makes for an interesting read. Templates of technical report, business report and research reports are also included in the book. This provides the reader with a hands-on experience.

The first statistics guide focussing on practical application to process control design and maintenance Statistics for Process Control Engineers is the only guide to statistics written by and for process control professionals. It takes a wholly practical approach to the subject. Statistics are applied throughout the life of a process control scheme – from assessing its economic benefit, designing inferential properties, identifying dynamic models, monitoring performance and diagnosing faults. This book addresses all of these areas and more. The book begins with an overview of various statistical applications in the field of process control, followed by discussions of data characteristics, probability functions, data presentation, sample size, significance testing and commonly used mathematical functions. It then shows how to select and fit a distribution to data, before moving on to the application of regression analysis and data reconciliation. The book is extensively illustrated throughout with line drawings, tables and equations, and features numerous worked examples. In addition, two appendices include the data used in the examples and an exhaustive catalogue of statistical distributions. The data and a simple-to-use software tool are available for download. The reader can thus reproduce all of the examples and then extend the same statistical techniques to real problems. Takes a back-to-basics approach with a focus on techniques that have immediate, practical, problem-solving applications for practicing engineers, as well as engineering students Shows how to avoid the many common errors made by the industry in applying statistics to process control Describes not only the well-known statistical distributions but also demonstrates the advantages of applying the large number that are less well-known Inspires engineers to identify new applications of statistical techniques to the design and support of control schemes Provides a deeper understanding of services and products which control engineers are often tasked with assessing This book is a valuable professional resource for engineers working in the global process industry and engineering companies, as well as students of engineering. It will be of great interest to those in the oil and gas, chemical, pulp and paper, water purification, pharmaceuticals and power generation industries, as well as for design engineers, instrument engineers and process technical support.

Analyze your biostatistics data with JMP! Trevor Bihl's Biostatistics Using JMP: A Practical Guide provides a practical introduction on using JMP, the interactive statistical discovery software, to solve biostatistical problems. Providing extensive breadth, from summary statistics to neural networks, this essential volume offers a comprehensive, step-by-step guide to using JMP to handle your data. The first biostatistical book to focus on software, Biostatistics Using JMP discusses such topics as data visualization, data wrangling, data cleaning, histograms, box plots, Pareto plots, scatter plots, hypothesis tests, confidence intervals, analysis of variance, regression, curve fitting, clustering, classification, discriminant analysis, neural networks, decision trees, logistic regression, survival analysis, control charts, and metaanalysis. Written for university students, professors, those who perform biological/biomedical experiments, laboratory managers, and research scientists, Biostatistics Using JMP provides a practical approach to using JMP to solve your biostatistical problems.

Discover how to unlock the secrets of time-series data with "Practical Time Series Analysis". With a focus on hands-on learning, this book takes you on a journey through time series data processing, visualization, and modeling. Gain the technical expertise and confidence to tackle real-world datasets using Python. What this Book will help me do Understand the fundamental principles of time series analysis and their application to real-world datasets. Learn to utilize Python for data preparation, visualization, and processing in the context of time series. Master the techniques of evaluating and addressing common challenges such as non-stationarity and autocorrelation. Apply statistical methods and machine learning models, including ARIMA and deep learning approaches, to forecasting tasks. Develop practical skills to implement and deploy end-to-end predictive models for time series data analysis. Author(s) PKS Prakash and Avishek Pal bring decades of combined experience in data science and analytics. Their meticulous approach toward simplifying complex concepts makes learning time series approachable and engaging. Drawing from their professional expertise, they incorporate extensive examples to merge theory with practice. Who is it for? This book is ideal for data scientists and engineers keen on enhancing their abilities to analyze temporal data. Prior knowledge in Python and basic statistics will help you gain the most from this book. Whether advancing your career or solving practical problems, you'll find invaluable insights here.

"Data Analysis with IBM SPSS Statistics" is a comprehensive guide designed to help you master IBM SPSS Statistics for performing robust statistical analyses. Through a practical approach, the book delves into critical techniques like data visualization, regression analysis, and hypothesis testing, enabling you to uncover patterns, make informed decisions, and enhance data interpretation. What this Book will help me do Set up and configure IBM SPSS Statistics for effective data analysis workflows. Perform data cleaning and preparation, including addressing missing data and restructuring datasets. Master statistical techniques such as ANOVA, regression analysis, and clustering to draw insights from data. Generate intuitive visualizations like charts and graphs to communicate findings effectively. Build predictive models and evaluate their effectiveness for decision-making purposes. Author(s) Ken Stehlik-Barry and Anthony Babinec are seasoned data analysts and IBM SPSS experts with extensive experience in statistical methodologies and data science. They have a knack for translating complex concepts into accessible lessons, making this book an ideal resource for learners aiming to build their SPSS aptitude. Their expertise ensures a well-rounded learning journey. Who is it for? This book is tailored for data analysts and researchers who need to analyze and interpret data effectively using IBM SPSS Statistics. Readers should have basic familiarity with statistical concepts, making it ideal for those with a foundational understanding of statistics. If you aim to grasp practical applications of SPSS for real-world data challenges, this book is for you.

If you have been frustrated by very technical statistical process control (SPC) training materials, then this is the book for you. This book focuses on how SPC works and why managers should consider using it in their operations. It provides you with a conceptual understanding of SPC so that appropriate decisions can be made about the benefits of incorporating SPC into the process management and quality improvement processes. Today there is little need to make the necessary calculations by hand, so the author utilizes Minitab and NWA Quality Analyst—two of the most popular statistical analysis software packages on the market. Links are provided to the home pages of these software packages where trial versions may be downloaded for evaluation and trial use. The book also addresses the question of why SPC should be considered for use, the process of implementing SPC, how to incorporate SPC into problem identification, problem solving, and the management and improvement of processes, products, and services.

"Matplotlib 2.x By Example" is your comprehensive guide to mastering data visualization in Python using the Matplotlib library. Through detailed explanations and hands-on examples, this book will teach you how to create stunning, insightful, and professional-looking visual representations of your data. You'll learn valuable skills tailored towards practical applications in science, marketing, and data analysis. What this Book will help me do Understand the core features of Matplotlib and how to use them effectively. Create professional 2D and 3D visualizations, such as scatter plots, line graphs, and more. Develop skills to transform raw data into meaningful insights through visualization. Enhance your data visualizations with interactive elements and animations. Leverage additional libraries such as Seaborn and Pandas to expand functionality. Author(s) Allen Yu, Claire Chung, and Aldrin Yim are seasoned data scientists and technical authors with extensive experience in Python and data visualization. Allen and his coauthors are dedicated to helping readers bridge the gap between their raw data and meaningful insights through visualization. With practical applications and real-world examples, their approachable writing makes complex libraries like Matplotlib accessible and production-ready. Who is it for? This book is perfect for data enthusiasts, analysts, and Python programmers looking to enhance their data visualization skills. Whether you're a professional aiming to create high-quality visual reports or a student eager to understand and present data effectively, this book provides practical and actionable insights. Basic Python knowledge is expected, while all Matplotlib-related aspects are thoroughly explained.

Create and publish your own interactive data visualization projects on the webâ??even if you have little or no experience with data visualization or web development. Itâ??s inspiring and fun with this friendly, accessible, and practical hands-on introduction. This fully updated and expanded second edition takes you through the fundamental concepts and methods of D3, the most powerful JavaScript library for expressing data visually in a web browser. Ideal for designers with no coding experience, reporters exploring data journalism, and anyone who wants to visualize and share data, this step-by-step guide will also help you expand your web programming skills by teaching you the basics of HTML, CSS, JavaScript, and SVG. Learn D3 4.xâ??the latest D3 versionâ??with downloadable code and over 140 examples Create bar charts, scatter plots, pie charts, stacked bar charts, and force-directed graphs Use smooth, animated transitions to show changes in your data Introduce interactivity to help users explore your data Create custom geographic maps with panning, zooming, labels, and tooltips Walk through the creation of a complete visualization project, from start to finish Explore inspiring case studies with nine accomplished designers talking about their D3-based projects

This book presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. The book covers foundational principles and statistical models as well as popular psychometric models. Throughout the text, procedures are illustrated using examples primarily from educational assessments. A supplementary website provides the datasets, WinBUGS code, R code, and Netica files used in the examples.

Focused Genograms introduces and provides a guide to constructing focused genograms through the lens of attachment theory.

Design and Analysis of Experiments, 9th Edition continues to help senior and graduate students in engineering, business, and statistics--as well as working practitioners--to design and analyze experiments for improving the quality, efficiency and performance of working systems. This bestselling text maintains its comprehensive coverage by including: new examples, exercises, and problems (including in the areas of biochemistry and biotechnology); new topics and problems in the area of response surface; new topics in nested and split-plot design; and the residual maximum likelihood method is now emphasized throughout the book.

"Python Web Scraping" is a practical guide to extracting and processing online data using the Python programming language. With this book, you'll learn step-by-step how to build web scrapers and crawlers that can handle a range of data sources and structures. After reading this, you will be equipped to tackle real-world web scraping challenges effectively. What this Book will help me do Learn how to extract structured data from standard webpages using Python. Gain proficiency with libraries such as Selenium and PyQt for handling dynamic and JavaScript-dependent content. Build concurrent scrapers to efficiently process large volumes of web pages in parallel. Understand and implement form interaction automation for data extraction from complex websites. Develop advanced scrapers using Scrapy to handle sophisticated web crawling tasks. Author(s) None Jarmul is an experienced data scientist and programmer with extensive knowledge in Python. They bring practical expertise from working on real-world web scraping projects. In their work, they focus on creating content that empowers readers by demystifying complex technical topics. Who is it for? This book is perfect for software developers eager to dive into web scraping using Python, even if they're new to the subject. If you have basic to intermediate Python skills and want to automate data collection and processing, this is the book for you. The techniques here are valuable for tackling diverse data extraction scenarios.

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code. IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. Conduct a more efficient and accurate analysis Display complex relationships and create better visualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient, more powerful code These "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need.

Learn to build beautiful and interactive data visualizations with D3.js 4.x in this approachable and practical guide. From setting up the basics to mastering advanced techniques, this book empowers you to create engaging, sophisticated graphics and charts to communicate information effectively. What this Book will help me do Effectively map data sets to visual representations using D3.js scales. Generate dynamic and interactive SVG elements with D3's shape generators. Develop reusable D3 components for extensible and testing-friendly charts. Leverage advanced D3 layout patterns for large-scale data visualization. Write modern, efficient JavaScript for web-based data visualization systems. Author(s) Aendrew Rininsland, an experienced data visualization developer, and co-author bring a wealth of expertise to this book, drawing from years of implementing interactive solutions in professional contexts. Their engaging way of explaining concepts and demonstrating techniques with practical examples makes learning straightforward and impactful. Who is it for? This book is ideal for web developers, data analysts, and designers who are interested in enhancing their web-based data visualization skills with D3.js. Some familiarity with JavaScript is beneficial but not strictly required, as the book provides clear guidance to bring readers up to speed. If you aspire to effectively present data through captivating visuals and improve your technical proficiency, this book is a great fit for you.

Research Methods in Human-Computer Interaction is a comprehensive guide to performing research and is essential reading for both quantitative and qualitative methods. Since the first edition was published in 2009, the book has been adopted for use at leading universities around the world, including Harvard University, Carnegie-Mellon University, the University of Washington, the University of Toronto, HiOA (Norway), KTH (Sweden), Tel Aviv University (Israel), and many others. Chapters cover a broad range of topics relevant to the collection and analysis of HCI data, going beyond experimental design and surveys, to cover ethnography, diaries, physiological measurements, case studies, crowdsourcing, and other essential elements in the well-informed HCI researcher's toolkit. Continual technological evolution has led to an explosion of new techniques and a need for this updated 2nd edition, to reflect the most recent research in the field and newer trends in research methodology. This Research Methods in HCI revision contains updates throughout, including more detail on statistical tests, coding qualitative data, and data collection via mobile devices and sensors. Other new material covers performing research with children, older adults, and people with cognitive impairments. Comprehensive and updated guide to the latest research methodologies and approaches, and now available in EPUB3 format (choose any of the ePub or Mobi formats after purchase of the eBook) Expanded discussions of online datasets, crowdsourcing, statistical tests, coding qualitative data, laws and regulations relating to the use of human participants, and data collection via mobile devices and sensors New material on performing research with children, older adults, and people with cognitive impairments, two new case studies from Google and Yahoo!, and techniques for expanding the influence of your research to reach non-researcher audiences, including software developers and policymakers

The right visual revealed at the right time can turn an unremarkable presentation into a resonant, emotional experience. This two-book collection provides you with the tools you need to craft and deliver presentations that will impress your audience, increase your influence in your organization, and advance your career. Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations shows how a good visualization can communicate the nature and potential impact of information and ideas more powerfully than any other form of communication. For a long time "dataviz" was left to specialists--data scientists and professional designers. No longer. A new generation of tools and massive amounts of available data make it easy for anyone to create visualizations that communicate ideas far more effectively than generic spreadsheet charts ever could. What's more, building good charts is quickly becoming a need-to-have skill for managers. If you're not doing it, other managers are, and they're getting noticed for it and getting credit for contributing to your company's success. In Good Charts, dataviz maven Scott Berinato provides an essential guide to how visualization works and how to use this new language to impress and persuade. Dataviz today is where spreadsheets and word processors were in the early 1980s—on the cusp of changing how we work. Berinato lays out a system for thinking visually and building better charts through a process of talking, sketching, and prototyping. This book is much more than a set of static rules for making visualizations. It taps into both well-established and cutting-edge research in visual perception and neuroscience, as well as the emerging field of visualization science, to explore why good charts (and bad ones) create "feelings behind our eyes." Along the way, Berinato also includes many engaging vignettes of dataviz pros, illustrating the ideas in practice. Good Charts will help you turn plain, uninspiring charts that merely present information into smart, effective visualizations that powerfully convey ideas. HBR Guide to Persuasive Presentations will teach you to how to take the pain out of presentations. Terrified of speaking in front of a group? Or simply looking to polish your skills? No matter where you are on the spectrum, this guide will give you the confidence and the tools you need to get results. Written by presentation expert Nancy Duarte, the HBR Guide to Persuasive Presentations will help you: (1) Win over tough crowds, (2) Organize a coherent narrative, (3) Create powerful messages and visuals, (4) Connect with and engage your audience, (5) Show people why your ideas matter to them, and (6) Strike the right tone, in any situation.

Budgeting, planning and forecasting are critical management tasks that not only impact the future success of an organization, but can threaten its very survival if done badly. Yet in spite of their importance, the speed and complexity of today’s business environment has caused a rapid decrease in the planning time horizon. As a consequence the traditional planning processes have become unsuitable for most organization’s needs. In this book readers will find new, original insights, including: 7 planning models that every organization needs to plan and manage performance 6 ways in which performance can be viewed A planning framework based on best management practices that can cope with an unpredictable business environment The application of technology to planning and latest developments in systems Results of the survey conducted for the book on the state of planning in organizations