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Data analysis : what can be learned from the past 50 years
This book explores the many provocative questions concerning the fundamentals of data analysis.It is based on the time-tested experience of one of the gurus of the subject matter.Why should one study data analysis?How should it be taught?What techniques work best, and for whom?How valid are the results?How much data should be tested?.
eBook
Modeling and analysis of compositional data
Modeling and Analysis of Compositional Data presents a practical and comprehensive introduction to the analysis of compositional data along with numerous examples to illustrate both theory and application of each method. Based upon short courses delivered by the authors, it provides a complete and current compendium of fundamental to advanced methodologies along with exercises at the end of each chapter to improve understanding, as well as data and a solutions manual which is available on an accompanying website.
Complementing Pawlowsky-Glahn's earlier collective text that provides an overview of the state-of-the-art in this field, Modeling and Analysis of Compositional Data fills a gap in the literature for a much-needed manual for teaching, self learning or consulting.
eBook
Advanced Mathematical Applications in Data Science
Advanced Mathematical Applications in Data Science comprehensively explores the crucial role mathematics plays in the field of data science. Each chapter is contributed by scientists, researchers, and academicians. The 13 chapters cover a range of mathematical concepts utilized in data science, enabling readers to understand the intricate connection between mathematics and data analysis. The book covers diverse topics, including, machine learning models, the Kalman filter, data modeling, artificial neural networks, clustering techniques, and more, showcasing the application of advanced mathematical tools for effective data processing and analysis. With a strong emphasis on real-world applications, the book offers a deeper understanding of the foundational principles behind data analysis and its numerous interdisciplinary applications. This reference is an invaluable resource for graduate students, researchers, academicians, and learners pursuing a research career in mathematical computing or completing advanced data science courses. Key Features: Comprehensive coverage of advanced mathematical concepts and techniques in data scienceContributions from established scientists, researchers, and academiciansReal-world case studies and practical applications of mathematical methodsFocus on diverse areas, such as image classification, carbon emission assessment, customer churn prediction, and healthcare data analysisIn-depth exploration of data science's connection with mathematics, computer science, and artificial intelligenceScholarly references for each chapterSuitable for readers with high school-level mathematical knowledge, making it accessible to a broad audience in academia and industry.
eBook
Handbook of Spatial Statistics
Based on the work of prominent researchers, this handbook provides broad, thorough coverage of this vibrant area, from historical to contemporary topics. It explores the modeling advances, computational approaches, and methodology that have emerged in recent years. The book focuses on continuous and discrete spatial variation, spatial point patterns, and spatio-temporal processes. It also covers multivariate spatial process models, spatial aggregation, spatial misalignment, and spatial gradients in depth. The theory and applications are illustrated with many real-world data examples.
eBook
Complex surveys
A complete guide to carrying out complex survey analysis using R As survey analysis continues to serve as a core component of sociological research, researchers are increasingly relying upon data gathered from complex surveys to carry out traditional analyses. Complex Surveys is a practical guide to the analysis of this kind of data using R, the freely available and downloadable statistical programming language. As creator of the specific survey package for R, the author provides the ultimate presentation of how to successfully use the software for analyzing data from complex surveys while also utilizing the most current data from health and social sciences studies to demonstrate the application of survey research methods in these fields. The book begins with coverage of basic tools and topics within survey analysis such as simple and stratified sampling, cluster sampling, linear regression, and categorical data regression. Subsequent chapters delve into more technical aspects of complex survey analysis, including post-stratification, two-phase sampling, missing data, and causal inference. Throughout the book, an emphasis is placed on graphics, regression modeling, and two-phase designs. In addition, the author supplies a unique discussion of epidemiological two-phase designs as well as probability-weighting for causal inference. All of the book's examples and figures are generated using R, and a related Web site provides the R code that allows readers to reproduce the presented content. Each chapter concludes with exercises that vary in level of complexity, and detailed appendices outline additional mathematical and computational descriptions to assist readers with comparing results from various software systems. Complex Surveys is an excellent book for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. It is also a practical reference guide for applied statisticians and practitioners in the social and health sciences who use statistics in their everyday work.
eBook