This book focuses on several skew-normal mixed effects models, and systematically explores the statistical inference theories, methods, and applications of parameters of interest. This book is of academic value, since it helps to establish a series of statistical inference theories and methods for skew-normal mixed effects models.

Fuzzy Methods for Assessment and Decision Making presents the assessment of learning and problem-solving skills with qualitative grades. These methods are outcomes of the author’s research work on the subject for more than 20 years. In particular, a hybrid assessment model uses the Center of Gravity (COG) defuzzification technique, closed real intervals (grey numbers), neutrosophic sets, and soft sets as tools. The book starts with the basic mathematical background that is needed for an understanding of its contents. The Rectangular Fuzzy Assessment Model (RFAM) of Subbotin and Voskoglou is presented next, the outcomes of which are compared to those of the GPA index. The book presents innovative fuzzy assessment methods, enabling readers to assess the mean and quality performance of learning or problem-solving skills of a group of students when qualitative (linguistic) grades are used for this purpose. In the case of using linguistic grades for the assessment of a group’s skills, the classical method of calculating the mean value of the (numerical) grades cannot be applied. Also, no safe conclusions can be obtained on comparing the quality performance of two groups when the values of their GPA index are equal. Presents innovative, fuzzy assessment methods to enable readers to assess the mean and quality performance of learning Discusses fuzzy logic and techniques for decision-making in all domains Includes applications of fuzzy decision-making as a hybrid model using soft sets, grey numbers, and neutrosophic sets

This book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians who have been working at the forefront of data analysis and related applications, arising from data science, operations research, engineering, machine learning or statistics. The chapters of this collaborative work represent a cross-section of current research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications. Data Analysis and Related Applications 4 investigates a number of different topics in the areas mentioned above, touching on statistical analysis, stochastic processes, estimation methods, algorithms, distributions and networks, among others.

Using an accessible, non-technical approach, the third edition of Regression Basics introduces readers to the fundamentals of statistical regression. Accessible to anyone with an introductory statistics background, the book draws on engaging examples using real-world data and software programs.

Great data presentations tell a story. Learn how to organize, visualize, and present data using Python, generative AI, and the cutting-edge Altair data visualization toolkit. Take the fast track to amazing data presentations! Data Storytelling with Altair and AI introduces a stack of useful tools and tried-and-tested methodologies that will rapidly increase your productivity, streamline the visualization process, and leave your audience inspired. In Data Storytelling with Altair and AI you’ll discover: Using Python Altair for data visualization Using Generative AI tools for data storytelling The main concepts of data storytelling Building data stories with the DIKW pyramid approach Transforming raw data into a data story Data Storytelling with Altair and AI teaches you how to turn raw data into effective, insightful data stories. You’ll learn exactly what goes into an effective data story, then combine your Python data skills with the Altair library and AI tools to rapidly create amazing visualizations. Your bosses and decision-makers will love your new presentations—and you’ll love how quick Generative AI makes the whole process! About the Technology Every dataset tells a story. After you’ve cleaned, crunched, and organized the raw data, it’s your job to share its story in a way that connects with your audience. Python’s Altair data visualization library, combined with generative AI tools like Copilot and ChatGPT, provide an amazing toolbox for transforming numbers, code, text, and graphics into intuitive data presentations. About the Book Data Storytelling with Altair and AI teaches you how to build enhanced data visualizations using these tools. The book uses hands-on examples to build powerful narratives that can inform, inspire, and motivate. It covers the Altair data visualization library, along with AI techniques like generating text with ChatGPT, creating images with DALL-E, and Python coding with Copilot. You’ll learn by practicing with each interesting data story, from tourist arrivals in Portugal to population growth in the USA to fake news, salmon aquaculture, and more. What's Inside The Data-Information-Knowledge-Wisdom (DIKW) pyramid Publish data stories using Streamlit, Tableau, and Comet Vega and Vega-Lite visualization grammar About the Reader For data analysts and data scientists experienced with Python. No previous knowledge of Altair or Generative AI required. About the Author Angelica Lo Duca is a researcher at the Institute of Informatics and Telematics of the National Research Council, Italy. The technical editor on this book was Ninoslav Cerkez. Quotes This book’s step-by-step approach, illustrated through real-world examples, makes complex data accessible and actionable. - Alexey Grigorev, DataTalks.Club A clear and concise guide to data storytelling. Highly recommended. - Andrew Madson, Insights x Design Data storytelling in a way that anyone can do! This book feels ahead of its time. - Avery Smith, Data Career Jumpstart Excellent hands-on exercises that combine two of my favorite tools: AI and the Altair library. - Jose Berengueres, Author of DataViz and Storytelling

The new edition of the bestselling Classification Methods for Remotely Sensed Data covers current state-of-the-art machine learning algorithms and developments in the analysis of remotely sensed data, and presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods.

Introductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration Statistics for Data Science and Analytics is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. A range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of “kitchen sink” formulas. Regression is taught both as a tool for explanation and for prediction. This book is informed by the authors’ experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. Statistics for Data Science and Analytics includes information on sample topics such as: Int, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and sets Experiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary data Specialized Python packages like numpy, scipy, pandas, scikit-learn and statsmodels—the workhorses of data science—and how to get the most value from them Statistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions Written by and for data science instructors, Statistics for Data Science and Analytics is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.

This revised and updated 8th edition retains its focus on processes that require understanding, have variation, must be properly controlled, have a capability, and need improvement – as reflected in the five sections of the book.

Graph Based Multimedia Analysis applies concepts from graph theory to the problems of analyzing overabundant video data. Video data can be quite diverse: exocentric (captured by a standard camera) or egocentric (captured by a wearable device like Google Glass); of various durations (ranging from a few seconds to several hours); and could be from a single source or multiple sources. Efficient extraction of important information from such a large class of diverse video data can be overwhelming. The book, with its rich repertoire of theoretically elegant solutions, from graph theory in conjunction with deep learning, constrained optimization, and game theory, empowers the audience to achieve tasks like obtaining concise yet useful summaries and precisely recognizing single as well as multiple actions in a computationally efficient manner. The book provides a unique treatise on topics like egocentric video analysis and scalable video processing. Addresses a number of challenging state-of-the-art problems in multimedia analysis like summarization, co-summarization, and action recognition Handles a wide class of video with different genres, durations, and numbers Applies a class of theoretically rich algorithms from the discipline of graph theory, in conjunction with deep learning, constrained optimization and game theory Includes thorough complexity analyses of the proposed solutions, and an appendix containing implementable source codes

The book aims to solve the discrete implementation problems of continuous-time neural network models while improving the performance of neural networks by using various Zhang Time Discretization (ZTD) formulas.

Fine-tune your marketing research with this cutting-edge statistical toolkit Bayesian Statistics and Marketing illustrates the potential for applying a Bayesian approach to some of the most challenging and important problems in marketing. Analyzing household and consumer data, predicting product performance, and custom-targeting campaigns are only a few of the areas in which Bayesian approaches promise revolutionary results. This book provides a comprehensive, accessible overview of this subject essential for any statistically informed marketing researcher or practitioner. Economists and other social scientists will find a comprehensive treatment of many Bayesian methods that are central to the problems in social science more generally. This includes a practical approach to computationally challenging problems in random coefficient models, non-parametrics, and the problems of endogeneity. Readers of the second edition of Bayesian Statistics and Marketing will also find: Discussion of Bayesian methods in text analysis and Machine Learning Updates throughout reflecting the latest research and applications Discussion of modern statistical software, including an introduction to the R package bayesm, which implements all models incorporated here Extensive case studies throughout to link theory and practice Bayesian Statistics and Marketing is ideal for advanced students and researchers in marketing, business, and economics departments, as well as for any statistically savvy marketing practitioner.

Can you forecast the future value by considering historical data? Accurate forecasting requires more than just plugging in historical data into models. Readers will find the latest techniques used by managers in business today, discover the importance of forecasting and learn how it is accomplished.

Break down biostatistics, make sense of complex concepts, and pass your class If you're taking biostatistics, you may need or want a little extra assistance as you make your way through. Biostatistics For Dummies follows a typical biostatistics course at the college level, helping you understand even the most difficult concepts, so you can get the grade you need. Start at the beginning by learning how to read and understand mathematical equations and conduct clinical research. Then, use your knowledge to analyze and graph your data. This new edition includes more example problems with step-by-step walkthroughs on how to use statistical software to analyze large datasets. Biostatistics For Dummies is your go-to guide for making sense of it all. Review basic statistics and decode mathematical equations Learn how to analyze and graph data from clinical research studies Look for relationships with correlation and regression Use software to properly analyze large datasets Anyone studying in clinical science, public health, pharmaceutical sciences, chemistry, and epidemiology-related fields will want this book to get through that biostatistics course.

Create stunning web-based data visualizations with D3.js. This totally-revised new edition of D3.js in Action guides you from simple charts to powerful interactive graphics. Chapter-by-chapter you’ll assemble an impressive portfolio of visualizations—including intricate networks, maps, and even a complete customized visualization layout. Plus, you'll learn best practices for building interactive graphics, animations, and integrating your work into frontend development frameworks like React and Svelte. In D3.js in Action, Third Edition you will learn how to: Set up a local development environment for D3 Include D3 in web development projects, including Node-based web apps Select and append DOM elements Size and position elements on screen Assemble components and layouts into creative data visualizations D3.js in Action, Third Edition has been extensively revised for D3.js version 7, and modern best practices for web visualizations. Its brand new chapters dive into interactive visualizations, cover responsiveness for dataviz, and show you how you can improve accessibility. About the Technology With D3.js, you can create sophisticated infographics, charts, and interactive data visualizations using standard frontend tools like JavaScript, HTML, and CSS. Granting D3 its VIS Test of Time award, the IEEE credited this powerful library for bringing data visualization to the mainstream. You’ll be blown away by how beautiful your results can be! About the Book D3.js in Action, Third Edition is a roadmap for creating brilliant and beautiful visualizations with D3.js. Like a gentle mentor, it guides you from basic charts all the way to advanced interactive visualizations like networks and maps. You’ll learn to build graphics, create animations, and set up mobile-friendly responsiveness. Each chapter contains a complete data visualization project to put your new skills into action. What's Inside Fully revised for D3.js v7 Includes 12 complete projects Create data visualizations with SVG and canvas Combine D3 with React, Svelte, and Angular About the Reader For web developers with HTML, CSS, and JavaScript skills. About the Authors Elijah Meeks was a data visualization pioneer at Stanford and the first Senior Data Visualization Engineer at Netflix. Anne-Marie Dufour is a Data Visualization Engineer. The technical editor on this book was Jon Borgman. Quotes Guides readers through the intricate world of D3 with clarity and practical insight. Whether you’re a seasoned expert or just starting, this book will be invaluable. - Connor Rothschild, Data Visualization Engineer, Moksha Data Studio Amazing job of explaining the core concepts of D3 while providing all you need to learn other fundamental concepts. - Lindsey Poulter, Visualization Engineer, New York Mets A navigation tool to explore all possible paths in the world of D3. Clear schematics and nicely selected examples guide the readers through D3’s possibilities. - Matthias Stahl, Head Data & Visualizations, Der SPIEGEL

The 'Tableau Certified Data Analyst Certification Guide' is your essential roadmap to mastering Tableau and excelling in the Tableau Data Analyst certification exam. From fundamentals to advanced techniques, you'll solidify your Tableau skills with clear explanations, practical exercises, and realistic mock exams. After reading, you'll be ready to take the next step in your data analytics career. What this Book will help me do Gain the ability to connect, clean, and transform data effectively using Tableau. Master Tableau's diverse calculation types for data analysis, ranging from basic to advanced. Develop skills to create visually impactful dashboards and data stories. Learn to publish and manage insights on Tableau Cloud for broader collaboration. Acquire the necessary competencies to confidently pass the Tableau Data Analyst certification exam. Author(s) Authors Harry Cooney and Daisy Jones bring a wealth of Tableau and data analytics experience. Harry is a certified Tableau expert with years of teaching and consulting, while Daisy applies her data analysis expertise across industries. Together, they combine practical insights and a supportive approach to guide you through Tableau mastery and certification. Who is it for? This book is ideal for aspiring and practicing data analysts eager to master Tableau. Beginners will appreciate the accessible approach to foundational concepts, while experienced users can deepen their expertise. If you're preparing for the Tableau Certified Data Analyst exam or looking to enhance your visual analytics capabilities, this book is for you.

Dive into the fascinating world of graph theory and its applications with 'Modern Graph Theory Algorithms with Python.' Through Python programming and real-world case studies, this book equips you with the tools to transform data into graph structures, apply algorithms, and uncover insights, enabling effective solutions in diverse domains such as finance, epidemiology, and social networks. What this Book will help me do Understand how to wrangle a variety of data types into network formats suitable for analysis. Learn to use graph theory algorithms and toolkits such as NetworkX and igraph in Python. Apply network theory to predict and analyze trends, from epidemics to stock market dynamics. Explore the intersection of machine learning and graph theory through advanced neural network techniques. Gain expertise in database solutions with graph database querying and applications. Author(s) Colleen M. Farrelly, an experienced data scientist, and Franck Kalala Mutombo, a seasoned software engineer, bring years of expertise in network science and Python programming to every page of this book. Their professional experience includes working on cutting-edge problems in data analytics, graph theory, and scalable solutions for real-world issues. Combining their practical know-how, they deliver a resource aimed at both learning and applying techniques effectively. Who is it for? This book is tailored for data scientists, researchers, and analysts with an interest in using graph-based approaches for solving complex data problems. Ideal for those with a basic Python knowledge and familiarity with libraries like pandas and NumPy, the content bridges the gap between theory and application. It also provides insights into broad fields where network science can be impactful, contributing value to both students and professionals.

This book is intended as an introduction to Probability Theory and Mathematical Statistics for students in mathematics, the physical sciences, engineering, and related fields. It is based on the author’s 25 years of experience teaching probability and is squarely aimed at helping students overcome common difficulties in learning the subject. The focus of the book is an explanation of the theory, mainly by the use of many examples. Whenever possible, proofs of stated results are provided. All sections conclude with a short list of problems. The book also includes several optional sections on more advanced topics. This textbook would be ideal for use in a first course in Probability Theory. Contents: Probabilities Conditional Probabilities and Independence Random Variables and Their Distribution Operations on Random Variables Expected Value, Variance, and Covariance Normally Distributed Random Vectors Limit Theorems Introduction to Stochastic Processes Mathematical Statistics Appendix Bibliography Index

The book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians who have been working at the forefront of data analysis and related applications, arising from data science, operations research, engineering, machine learning or statistics. The chapters of this collaborative work represent a cross-section of current research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications. The published data analysis methodology includes the updated state-of-the-art rapidly developed theory and applications of data expansion, both of which go through outstanding changes nowadays. New approaches are expected to deliver and have been developed, including Artificial Intelligence.

This book covers the key principles, best practices, and practical techniques for designing and implementing visually compelling dashboards. It explores the various stages of the dashboard development process, from understanding user needs and defining goals, to selecting appropriate visual encodings, designing effective layouts, and employing interactive elements. It also addresses the critical aspect of data storytelling, examining how narratives and context can be woven into dashboards to deliver impactful insights and engage audiences. Visual Analytics for Dashboards is designed to cater to a wide range of readers, from beginners looking to grasp the fundamentals of visual analytics, to seasoned professionals seeking to enhance their dashboard design skills. For different types of readers, such as a data analyst, BI professional, data scientist, or simply someone interested in data visualization, this book aims to equip them with the knowledge and tools necessary to create impactful dashboards. What you’ll learn The principles of data visualization How to create effective dashboards Meet all the requirements for visual analytics/data visualization/dashboard courses Deepen understanding of data presentation and analysis How to use different kinds of tools for data analysis, such as scorecards and key performance indicators Who This Book Is For Business analysts, data analysts, BI professionals, end-users, executives, developers, as well as students in dashboards, data visualizations, and visual analytics courses.

One of the most influential data visualization books—updated with new techniques, technologies, and examples Visualize This demonstrates how to explain data visually, so that you can present and communicate information in a way that is appealing and easy to understand. Today, there is a continuous flow of data available to answer almost any question. Thoughtful charts, maps, and analysis can help us make sense of this data. But the data does not speak for itself. As leading data expert Nathan Yau explains in this book, graphics provide little value unless they are built upon a firm understanding of the data behind them. Visualize This teaches you a data-first approach from a practical point of view. You'll start by exploring what your data has to say, and then you'll design visualizations that are both remarkable and meaningful. With this book, you'll discover what tools are available to you without becoming overwhelmed with options. You'll be exposed to a variety of software and code and jump right into real-world datasets so that you can learn visualization by doing. You'll learn to ask and answer questions with data, so that you can make charts that are both beautiful and useful. Visualize This also provides you with opportunities to apply what you learn to your own data. This completely updated, full-color second edition: Presents a unique approach to visualizing and telling stories with data, from data visualization expert Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design Details tools that can be used to visualize data graphics for reports, presentations, and stories, for the web or for print, with major updates for the latest R packages, Python libraries, JavaScript libraries, illustration software, and point-and-click applications Contains numerous examples and descriptions of patterns and outliers and explains how to show them Information designers, analysts, journalists, statisticians, data scientists—as well as anyone studying for careers in these fields—will gain a valuable background in the concepts and techniques of data visualization, thanks to this legendary book.

This thoroughly updated and expanded second edition covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and big data have been added. Core classical nonparametrics chapters on one- and two-sample problems have been expanded

The problem of forecasting future values of economic and physical processes, the problem of restoring lost information, cleaning signals or other data observations from noise, is magnified in an information-laden word. Methods of stochastic processes estimation depend on two main factors. The first factor is construction of a model of the process being investigated. The second factor is the available information about the structure of the process under consideration. In this book, we propose results of the investigation of the problem of mean square optimal estimation (extrapolation, interpolation, and filtering) of linear functionals depending on unobserved values of stochastic sequences and processes with periodically stationary and long memory multiplicative seasonal increments. Formulas for calculating the mean square errors and the spectral characteristics of the optimal estimates of the functionals are derived in the case of spectral certainty, where spectral structure of the considered sequences and processes are exactly known. In the case where spectral densities of the sequences and processes are not known exactly while some sets of admissible spectral densities are given, we apply the minimax-robust method of estimation.

The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies. If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud. Explore ways that predictive analytics can provide direct input back to your business Understand mathematical tools commonly used in predictive analytics Learn the development frameworks used in predictive analytics applications Appreciate the role of predictive analytics in the machine learning process Examine industry implementations of predictive analytics Build, train, and retrain predictive models using Python and TensorFlow

This book is designed to meet the requirements of students and is intended for beginners to help them understand the concepts from the first principles. Spread across 16 chapters, it discusses the theoretical aspects that have been refined and updated to reflect the current developments in the subjects. It expounds on theoretical concepts that have immense practical applications, giving adequate proofs to establish significant theorems.

Book Contents –

Chapter 1: Probability Chapter 2: Random Variables (Discrete and Continuous) Chapter 3: Mathematical Expectation Chapter 4: Standard Discrete Distributions Chapter 5: Standard Continuous Distributions Chapter 6: Sampling Theory and Distribution Chapter 7: Testing of Hypothesis (Large Samples) Chapter 8: Test of Hypothesis (Small Samples) Chapter 9: Estimation Chapter 10: Curve Fitting Chapter 11: Correlation Chapter 12: Regression Chapter 13: Queuing Theory Chapter 14: Design of Experiments Chapter 15: Random Process Chapter 16: Advanced Random Process Appendix A, Appendix B, Appendix C, Appendix D Index

In today's data-driven world, understanding statistical models is crucial for effective analysis and decision making. Whether you're a beginner or an experienced user, this book equips you with the foundational knowledge to grasp and implement statistical models within Tableau. Gain the confidence to speak fluently about the models you employ, driving adoption of your insights and analysis across your organization. As AI continues to revolutionize industries, possessing the skills to leverage statistical models is no longer optional—it's a necessity. Stay ahead of the curve and harness the full potential of your data by mastering the ability to interpret and utilize the insights generated by these models. Whether you're a data enthusiast, analyst, or business professional, this book empowers you to navigate the ever-evolving landscape of data analytics with confidence and proficiency. Start your journey toward data mastery today. In this book, you will learn: The basics of foundational statistical modeling with Tableau How to prove your analysis is statistically significant How to calculate and interpret confidence intervals Best practices for incorporating statistics into data visualizations How to connect external analytics resources from Tableau using R and Python