Practical Data Processing and Analysis Using R

A practical book for professionals who rely on water quality data for decision making, this book is based on three decades experience of three highly published water and watershed resource professiona

Get the expert perspective and practical advice on big dataThe Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits makes the case that big data is for r

Guerci presents engineers and practitioners with analysis tools for assessing the impact of space-time adaptive processing (STAP) on radar applications, as well as a toolkit of STAP algorithms and tec

This book covers EEG/ERP processing and analysis and their various applications. It explains the different techniques used for preprocessing the EEG/ERP data for artifacts removal and the advanced tec

"Enabling readers to quickly implement solutions with respect to collecting, licensing, handling, and using Big Data, this book provides business people and lawyers with an accessible handbook primer

The analysis of recurrences in dynamical systems by using recurrence plots and their quantification is still an emerging field. Over the past decades recurrence plots have proven to be valuable data v

Learn how to discover and present quantitative truths with data, using the fast-growing data visualization software Tableau. This practical guide takes you step-by-step through a process of converting

This unique text/reference highlights a selection of practical applications of advanced image analysis methods for medical images. The book covers the complete methodology for processing, analysing an

This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of modeling count data, including a thorough presentation of the Poisson model. It then works up to an analysis of the problem of overdispersion and of the negative binomial model, and finally to the many variations that can be made to the base count models. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in health, ecology, econometrics, transportation, and other fields.

This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of modeling count data, including a thorough presentation of the Poisson model. It then works up to an analysis of the problem of overdispersion and of the negative binomial model, and finally to the many variations that can be made to the base count models. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in health, ecology, econometrics, transportation, and other fields.

This guide is ideal if you’re a professional, manager, or student who wants practical knowledge of analyzing data, without having to get a PhD in statistics. It’s also good for people who have a PhD i

This is the fourth edition of a comprehensive practical guide, reference book and course textbook. It is designed for use in a survey course about psychopathology. Each of the core chapters is essenti

Striking a balance between theory and practice, this graduate-level text is perfect for students in the applied sciences. The author provides a clear introduction to the classical methods, how they work and why they sometimes fail. Crucially, he also demonstrates how these simple and classical techniques can be combined to address difficult problems. Many worked examples and sample programs are provided to help the reader make practical use of the subject material. Further mathematical background, if required, is summarized in an appendix. Topics covered include classical methods for linear systems, eigenvalues, interpolation and integration, ODEs and data fitting, and also more modern ideas like adaptivity and stochastic differential equations.

Bioinformatics encompasses a broad and ever-changing range of activities involved with the management and analysis of data from molecular biology experiments. Despite the diversity of activities and a

Stream processing is a novel distributed computing paradigm that supports the gathering, processing and analysis of high-volume, heterogeneous, continuous data streams, to extract insights and actionable results in real time. This comprehensive, hands-on guide combining the fundamental building blocks and emerging research in stream processing is ideal for application designers, system builders, analytic developers, as well as students and researchers in the field. This book introduces the key components of the stream computing paradigm, including the distributed system infrastructure, the programming model, design patterns and streaming analytics. The explanation of the underlying theoretical principles, illustrative examples and implementations using the IBM InfoSphere Streams SPL language and real-world case studies provide students and practitioners with a comprehensive understanding of such applications and the middleware that supports them.

Written for animal researchers, this book provides a comprehensive guide to the design and statistical analysis of animal experiments. It has long been recognised that the proper implementation of these techniques helps reduce the number of animals needed. By using real-life examples to make them more accessible, this book explains the statistical tools employed by practitioners. A wide range of design types are considered, including block, factorial, nested, cross-over, dose-escalation and repeated measures and techniques are introduced to analyse the experimental data generated. Each analysis technique is described in non-mathematical terms, helping readers without a statistical background to understand key techniques such as t-tests, ANOVA, repeated measures, analysis of covariance, multiple comparison tests, non-parametric and survival analysis. This is also the first text to describe technical aspects of InVivoStat, a powerful open-source software package developed by the authors

Written for animal researchers, this book provides a comprehensive guide to the design and statistical analysis of animal experiments. It has long been recognised that the proper implementation of these techniques helps reduce the number of animals needed. By using real-life examples to make them more accessible, this book explains the statistical tools employed by practitioners. A wide range of design types are considered, including block, factorial, nested, cross-over, dose-escalation and repeated measures and techniques are introduced to analyse the experimental data generated. Each analysis technique is described in non-mathematical terms, helping readers without a statistical background to understand key techniques such as t-tests, ANOVA, repeated measures, analysis of covariance, multiple comparison tests, non-parametric and survival analysis. This is also the first text to describe technical aspects of InVivoStat, a powerful open-source software package developed by the authors

A concise, hands-on guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world. Whether you are an undergra

If you’re using JavaScript on the server-side, you need to implement classic data structures that conventional object-oriented programs (such as C# and Java) provide. This practical book shows you how

This guide explains how to apply health economic evaluation techniques to both clinical trial and non-clinical trial data. Through a simple, applied approach using examples and SAS software, the book

Suitable for researchers and graduate students in the social sciences, this book shows how to apply spatial models using R to a wide range of data from political science and related areas. It also cov

This is a practical step-by-step guide to using Excel in the data preparation process. The book includes plentiful screenshots, text boxes summarizing important points, examples from across the social

This book provides a reference guide to statistical tests and their application to data using SAS and R. A general summary of statistical test theory is presented, along with a general description for

SPREADSHEET MODELING AND DECISION ANALYSIS, Seventh Edition, provides instruction in the most commonly used management science techniques and shows how these tools can be implemented using Microsoft O

Large-scale data analysis ("Big Data") is suddenly of crucial importance to virtually every enterprise. Mobile and social technologies are generating massive datasets, and distributed cloud computing

Providing the knowledge and practical experience to begin analysing scientific data, this book is ideal for physical sciences students wishing to improve their data handling skills. The book focuses on explaining and developing the practice and understanding of basic statistical analysis, concentrating on a few core ideas, such as the visual display of information, modelling using the likelihood function, and simulating random data. Key concepts are developed through a combination of graphical explanations, worked examples, example computer code and case studies using real data. Students will develop an understanding of the ideas behind statistical methods and gain experience in applying them in practice. Further resources are available at www.cambridge.org/9781107607590, including data files for the case studies so students can practise analysing data, and exercises to test students' understanding.

Providing the knowledge and practical experience to begin analysing scientific data, this book is ideal for physical sciences students wishing to improve their data handling skills. The book focuses on explaining and developing the practice and understanding of basic statistical analysis, concentrating on a few core ideas, such as the visual display of information, modelling using the likelihood function, and simulating random data. Key concepts are developed through a combination of graphical explanations, worked examples, example computer code and case studies using real data. Students will develop an understanding of the ideas behind statistical methods and gain experience in applying them in practice. Further resources are available at www.cambridge.org/9781107607590, including data files for the case studies so students can practise analysing data, and exercises to test students' understanding.

The challenge behind the processing of digital images is the huge amounts of data that has to be processed in an extremely short period of time. This book is a broad-ranging technical survey of comput

If you have basic programming skills and do any kind of data analysis, you need R in your toolkit. This book teaches you how to be productive with this important programming language for statistics by

Mixed modeling is one of the most promising and exciting areas of statistical analysis, enabling the analysis of nontraditional, clustered data that may come in the form of shapes or images. This book

Written in simple language with relevant examples, this illustrative introductory book presents best practices in experimental design and simple data analysis. Taking a practical and intuitive approac

The massive amount of nonstandard high-dimensional brain imaging data being generated is often difficult to analyze using current techniques. This challenge in brain image analysis requires new comput

This book covers both fundamental and practical aspects of chemical analysis: Data Process and Analysis; Chemical Equilibria and Volumetric titraitons; Gravimetry; Spectrophotometry; Sample Preparatio

3D Face Modeling, Analysis and Recognition presents methodologies for analyzing shapes of facial surfaces, develops computational tools for analyzing 3D face data, and illustrates them using state-of-

This comprehensive introduction to the principles of functional programming using F# shows how to apply basic theoretical concepts to produce succinct and elegant programs. It demonstrates the role of functional programming in a wide spectrum of applications including databases and systems. Coverage also includes advanced features in the .NET library, the imperative features of F# and topics such as text processing, sequences, computation expressions and asynchronous computation. With a broad spectrum of examples and exercises, the book is perfect for courses in functional programming and for self-study. Enhancing its use as a text is an accompanying website with downloadable programs, lecture slides, mini-projects and links to further F# sources.

This comprehensive introduction to the principles of functional programming using F# shows how to apply basic theoretical concepts to produce succinct and elegant programs. It demonstrates the role of functional programming in a wide spectrum of applications including databases and systems. Coverage also includes advanced features in the .NET library, the imperative features of F# and topics such as text processing, sequences, computation expressions and asynchronous computation. With a broad spectrum of examples and exercises, the book is perfect for courses in functional programming and for self-study. Enhancing its use as a text is an accompanying website with downloadable programs, lecture slides, mini-projects and links to further F# sources.

A practical tutorial covering how to leverage RStudio functionality to effectively perform R Development, analysis, and reporting with RStudio. The book is aimed at R developers and analysts who wish

If you’re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and softwar

Multiscale Signal Analysis and Modeling presents recent advances in multiscale analysis and modeling using wavelets and other systems. This book also presents applications in digital signal processing