Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.
New to the Second Edition
The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows
New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics
New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping
New chapter on simulation that includes examples of data generated from complex models and distributions
A detailed discussion of the philosophy and use of the knitr and markdown packages for R
New packages that extend the functionality of R and facilitate sophisticated analyses
Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots
Easily Find Your Desired Task
Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.
Название: Open source software for statistical analysis of big data ISBN: 1799827682 ISBN-13(EAN): 9781799827689 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 201430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Presents research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. The book features coverage on a broad range of topics, including cluster analysis, time series forecasting, and machine learning.
Автор: Chinmay Chakraborty Название: Advanced Classification Techniques for Healthcare Analysis ISBN: 1522577963 ISBN-13(EAN): 9781522577966 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 238260.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Medical and information communication technology professionals are working to develop robust classification techniques, especially in healthcare data/image analysis, to ensure quick diagnoses and treatments to patients. Without fast and immediate access to healthcare databases and information, medical professionals’ success rates and treatment options become limited and fall to disastrous levels.Advanced Classification Techniques for Healthcare Analysis provides emerging insight into classification techniques in delivering quality, accurate, and affordable healthcare, while also discussing the impact health data has on medical treatments. Featuring coverage on a broad range of topics such as early diagnosis, brain-computer interface, metaheuristic algorithms, clustering techniques, learning schemes, and mobile telemedicine, this book is ideal for medical professionals, healthcare administrators, engineers, researchers, academicians, and technology developers seeking current research on furthering information and communication technology that improves patient care.
This book describes how the creation of new digital services--through vertical and horizontal integration of data coming from sensors on top of existing legacy systems--that has already had a major impact on industry is now extending to healthcare. The book describes the fourth industrial revolution (i.e. Health 4.0), which is based on virtualization and service aggregation. It shows how sensors, embedded systems, and cyber-physical systems are fundamentally changing the way industrial processes work, their business models, and how we consume, while also affecting the health and care domains. Chapters describe the technology behind the shift of point of care to point of need and away from hospitals and institutions; how care will be delivered virtually outside hospitals; that services will be tailored to individuals rather than being designed as statistical averages; that data analytics will be used to help patients to manage their chronic conditions with help of smart devices; and that pharmaceuticals will be interactive to help prevent adverse reactions. The topics presented will have an impact on a variety of healthcare stakeholders in a continuously global and hyper-connected world.
- Presents explanations of emerging topics as they relate to e-health, such as Industry 4.0, Precision Medicine, Mobile Health, 5G, Big Data, and Cyber-physical systems;
- Provides overviews of technologies in addition to possible application scenarios and market conditions;
- Features comprehensive demographic and statistic coverage of Health 4.0 presented in a graphical manner.
Автор: Mervyn G. Marasinghe; Kenneth J. Koehler Название: Statistical Data Analysis Using SAS ISBN: 3319692380 ISBN-13(EAN): 9783319692388 Издательство: Springer Рейтинг: Цена: 79190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data.
The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude.
Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem.
New to this edition:
• Covers SAS v9.2 and incorporates new commands • Uses SAS ODS (output delivery system) for reproduction of tables and graphics output • Presents new commands needed to produce ODS output • All chapters rewritten for clarity • New and updated examples throughout • All SAS outputs are new and updated, including graphics • More exercises and problems • Completely new chapter on analysis of nonlinear and generalized linear models • Completely new appendix
Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing.
Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.
Автор: Francesco Mola; Claudio Conversano; Maurizio Vichi Название: Classification, (Big) Data Analysis and Statistical Learning ISBN: 3319557076 ISBN-13(EAN): 9783319557076 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas such as economics, marketing, education, social sciences, medicine, environmental sciences and the pharmaceutical industry. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field. The peer-reviewed contributions were presented at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in Santa Margherita di Pula (Cagliari), Italy, October 8–10, 2015.
Автор: Anastasius Moumtzoglou Название: Design, Development, and Integration of Reliable Electronic Healthcare Platforms ISBN: 1522517243 ISBN-13(EAN): 9781522517245 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 171380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Explores the issues relating to the quality and safety of technology use in the medical realm. Featuring coverage on best practices, detailed analysis, and upcoming trends, this publication is essential for researchers, students and professionals seeking current research on the implementation of electronic technologies in healthcare.
Название: Open source software for statistical analysis of big data ISBN: 1799827690 ISBN-13(EAN): 9781799827696 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 152460.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Presents research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. The book features coverage on a broad range of topics, such as cluster analysis, time series forecasting, and machine learning.
Автор: Robert Nisbet , Gary Miner, Ken Yale Название: Handbook of Statistical Analysis and Data Mining Applications, 2 ed. ISBN: 0124166326 ISBN-13(EAN): 9780124166325 Издательство: Elsevier Science Рейтинг: Цена: 88690.00 T Наличие на складе: Поставка под заказ. Описание:
Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application.
This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas--from science and engineering, to medicine, academia and commerce.
Includes input by practitioners for practitioners
Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models
Contains practical advice from successful real-world implementations
Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions
Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications
Автор: Saito Название: Data Analysis of Asymmetric Structures ISBN: 0824753984 ISBN-13(EAN): 9780824753986 Издательство: Taylor&Francis Рейтинг: Цена: 148010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Offers a comprehensive presentation of a variety of models and theories for the analysis of asymmetry and its applications. This work details theories, methods, and models for the analysis of asymmetric structures in a variety of disciplines and presents opportunities and challenges affecting research developments and business applications.
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