Practical Data Processing and Analysis Using R

Noted for its accessible approach, this text applies the latest approaches of power analysis to both null hypothesis and minimum-effect testing using the same basic unified model. Through the use of a

Use Excel 2013’s statistical tools to transform your data into knowledgeConrad Carlberg shows how to use Excel 2013 to perform core statistical tasks every business professional, student, and research

Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.

Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.

On average, large companies have 178 corporate accounts on various social networks, and the data they collect is exploding more and more each year. Sound vaguely familiar? This practical book—written

This bestselling textbook is the ideal companion to An Introduction to Discourse Analysis: Theory and Method, by leading author, James Paul Gee. Using a practical how-to approach, Gee provides the too

This bestselling textbook is the ideal companion to An Introduction to Discourse Analysis: Theory and Method, by leading author, James Paul Gee. Using a practical how-to approach, Gee provides the too

The Seismic Analysis Code (SAC) is one of the most widely used analysis packages for regional and teleseismic seismic data. For the first time, this book provides users at introductory and advanced levels with a complete guide to SAC. It leads new users of SAC through the steps of learning basic commands, describes the SAC processing philosophy, and presents its macro language in full, supported throughout with example inputs and outputs from SAC. For more experienced practitioners, the book describes SAC's many hidden features, including advanced graphics aspects, its file structure, how to write independent programs to access and create files, and much more. Tutorial exercises engage users with newly acquired skills, providing data and code to implement the standard methods of teleseismic shear-wave splitting and receiver function analysis. Methodical and authoritative, this is a key resource for researchers and graduate students in global seismology, earthquake seismology and geophy

The Seismic Analysis Code (SAC) is one of the most widely used analysis packages for regional and teleseismic seismic data. For the first time, this book provides users at introductory and advanced levels with a complete guide to SAC. It leads new users of SAC through the steps of learning basic commands, describes the SAC processing philosophy, and presents its macro language in full, supported throughout with example inputs and outputs from SAC. For more experienced practitioners, the book describes SAC's many hidden features, including advanced graphics aspects, its file structure, how to write independent programs to access and create files, and much more. Tutorial exercises engage users with newly acquired skills, providing data and code to implement the standard methods of teleseismic shear-wave splitting and receiver function analysis. Methodical and authoritative, this is a key resource for researchers and graduate students in global seismology, earthquake seismology and geophy

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.

This comprehensive, up-to-date guide covers diagnosis, clinical features and management of inherited disorders conferring cancer susceptibility. Includes risk analysis, genetic counselling, predictive

A Teacher's Guide to Using the Common Core State Standards With Mathematically Gifted and Advanced Learners provides teachers and administrators with practical examples of ways to build a comprehensiv

Rasch Analysis in the Human Sciences helps individuals, both students and researchers, master the key concepts and resources needed to use Rasch techniques for analyzing data from assessments to measu

The Elements of Cognitive Aging provides a qualitative overview (mostly using graphical meta-analysis) of the vast literature on aging and speeded tasks-bringing together, for the first time, almost e

In recent years, cryptographic techniques for protecting and hiding secret information have been included in directions of research on intelligent information management. Data can be managed securely

This single-volume guide equips students of sociolinguistics with a full set of methodological tools including data collection and analysis techniques, explained in clear and accessible terms by leadi

This single-volume guide equips students of sociolinguistics with a full set of methodological tools including data collection and analysis techniques, explained in clear and accessible terms by leadi

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

Newcomers to quantitative analysis need practical guidance on how to analyze data in the real world yet most introductory books focus on lengthy derivations and justifications instead of practical tec

A biostatistician at the City of Hope National Medical Center in California's Los Angeles County, Li also teaches part time and gives seminars on DATA step programming and statistical analysis using t

Use real data, and become a pro at using Excel for statistical analysis! Designed for users already familiar with Excel and basic computer operations, this Second Edition of Neil J. Salkind’s Excel St

Aimed at graduates and researchers, and requiring only a basic knowledge of multi-variable calculus, this introduction to computer-based partial differential equation (PDE) modeling provides readers with the practical methods necessary to develop and use PDE mathematical models in biomedical engineering. Taking an applied approach, rather than using abstract mathematics, the reader is instructed through six biomedical example applications, each example characterized by step-by-step discussions of established numerical methods and implemented in reliable computer routines. Adopting this technique, the reader will understand how PDE models are formulated, implemented and tested. Supported by a set of rigorously tested general purpose PDE routines online, and with enhanced understanding through animations, this book will be ideal for anyone faced with interpreting large experimental data sets that need to be analyzed with PDE models in biomedical engineering.

Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public domain R statistical software environment. The book presents fundamental results of probability theory and statistical inference, before exploring several fields of applied statistics, such as data smoothing, regression, multivariate analysis and classification, treatment of nondetections, time series analysis, and spatial point processes. It applies the methods discussed to contemporary astronomical research datasets using the R statistical software, making it invaluable for graduate students and researchers facing complex data analysis tasks. A link to the author's website for this book ca

The small sample properties of estimators and tests are frequently too complex to be useful or are unknown. Much econometric theory is therefore developed for very large or asymptotic samples where it is assumed that the behaviour of estimators and tests will adequately represent their properties in small samples. Refined asymptotic methods adopt an intermediate position by providing improved approximations to small sample behaviour using asymptotic expansions. Dedicated to the memory of Michael Magdalinos, whose work is a major contribution to this area, this book contains chapters directly concerned with refined asymptotic methods. In addition, there are chapters focusing on new asymptotic results; the exploration through simulation of the small sample behaviour of estimators and tests in panel data models; and improvements in methodology. With contributions from leading econometricians, this collection will be essential reading for researchers and graduate students concerned with th

This book was first published in 2004. Many observed phenomena, from the changing health of a patient to values on the stock market, are characterised by quantities that vary over time: stochastic processes are designed to study them. This book introduces practical methods of applying stochastic processes to an audience knowledgeable only in basic statistics. It covers almost all aspects of the subject and presents the theory in an easily accessible form that is highlighted by application to many examples. These examples arise from dozens of areas, from sociology through medicine to engineering. Complementing these are exercise sets making the book suited for introductory courses in stochastic processes. Software (available from www.cambridge.org) is provided for the freely available R system for the reader to apply to all the models presented.

Astronomy needs statistical methods to interpret data, but statistics is a many-faceted subject that is difficult for non-specialists to access. This handbook helps astronomers analyze the complex data and models of modern astronomy. This second edition has been revised to feature many more examples using Monte Carlo simulations, and now also includes Bayesian inference, Bayes factors and Markov chain Monte Carlo integration. Chapters cover basic probability, correlation analysis, hypothesis testing, Bayesian modelling, time series analysis, luminosity functions and clustering. Exercises at the end of each chapter guide readers through the techniques and tests necessary for most observational investigations. The data tables, solutions to problems, and other resources are available online at www.cambridge.org/9780521732499. Bringing together the most relevant statistical and probabilistic techniques for use in observational astronomy, this handbook is a practical manual for advanced und

Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining by Glenn J. Myatt (978-0-470-07471-8), Making Sense of Data II: A Practical Guide to Data Visualization, Advanced D

The need to transmit and store massive amounts of data reliably and without error is a vital part of modern communications systems. Error-correcting codes play a fundamental role in minimising data corruption caused by defects such as noise, interference, crosstalk and packet loss. This book provides an accessible introduction to the basic elements of algebraic codes, and discusses their use in a variety of applications. The author describes a range of important coding techniques, including Reed-Solomon codes, BCH codes, trellis codes, and turbocodes. Throughout the book, mathematical theory is illustrated by reference to many practical examples. The book was first published in 2003 and is aimed at graduate students of electrical and computer engineering, and at practising engineers whose work involves communications or signal processing.

Behavioral scientists – including those in psychology, infant and child development, education, animal behavior, marketing and usability studies – use many methods to measure behavior. Systematic observation is used to study relatively natural, spontaneous behavior as it unfolds sequentially in time. This book emphasizes digital means to record and code such behavior; while observational methods do not require them, they work better with them. Key topics include devising coding schemes, training observers and assessing reliability, as well as recording, representing and analyzing observational data. In clear and straightforward language, this book provides a thorough grounding in observational methods along with considerable practical advice. It describes standard conventions for sequential data and details how to perform sequential analysis with a computer program developed by the authors. The book is rich with examples of coding schemes and different approaches to sequential analysis

Behavioral scientists – including those in psychology, infant and child development, education, animal behavior, marketing and usability studies – use many methods to measure behavior. Systematic observation is used to study relatively natural, spontaneous behavior as it unfolds sequentially in time. This book emphasizes digital means to record and code such behavior; while observational methods do not require them, they work better with them. Key topics include devising coding schemes, training observers and assessing reliability, as well as recording, representing and analyzing observational data. In clear and straightforward language, this book provides a thorough grounding in observational methods along with considerable practical advice. It describes standard conventions for sequential data and details how to perform sequential analysis with a computer program developed by the authors. The book is rich with examples of coding schemes and different approaches to sequential analysis

This book is a snapshot of current research in multiscale modeling, computations and applications. It covers fundamental mathematical theory, numerical algorithms as well as practical computational ad

For office professionals and business students, this guide to financial planning and analysis with Microsoft Excel 2010 provides practical example-based instruction for performing common enterprise ta

The success of the Apgar score demonstrates the astounding power of an appropriate clinical instrument. This down-to-earth book provides practical advice, underpinned by theoretical principles, on developing and evaluating measurement instruments in all fields of medicine. It equips you to choose the most appropriate instrument for specific purposes. The book covers measurement theories, methods and criteria for evaluating and selecting instruments. It provides methods to assess measurement properties, such as reliability, validity and responsiveness, and interpret the results. Worked examples and end-of-chapter assignments use real data and well-known instruments to build your skills at implementation and interpretation through hands-on analysis of real-life cases. All data and solutions are available online. This is a perfect course book for students and a perfect companion for professionals/researchers in the medical and health sciences who care about the quality and meaning of the

An up-to-date, detailed set of notes covering all aspects of NOAA AVHRR data collection, pre-processing, analysis and application. Includes many FTP sites, e-mail addresses and URL locations. Some cha

This comprehensive textbook is intended for a two-semester sequence in analysis. The first four chapters present a practical introduction to analysis by using the tools and concepts of calculus. The l

This class-tested book equips readers with the fundamental statistical practices that are essential for organizing, understanding, and drawing conclusions from educational research data. It is the fir

This book provides a practical approach to modeling and analyzing communication protocols using UML. It shows how to describe and validate main protocol issues (such as synchronization problems, clien

Originally published in 1985, this is the second of a series of handbooks sponsored by the Esturaine and Brackish-Water Sciences association. The book concentrates on the techniques and practicalities of data collection from the estuarine environment. It is intended that the information presented will increase the reader's understanding of estuarine processes thus enabling him to devise sensible sampling programmes and to interpret the results once obtained. Techniques discussed include trace element and organic matter analysis, use of ion-selective electrodes, and measurement of salinity, dissolved oxygen and nutrients. The text will be of primary relevance for aquatic chemists, but should also be of use to biologists, physicists, geologists and oceanographers working in estuaries.

In Using Statistical Methods, Soleman Abu-Bader detects and addresses the gaps between the research and data analysis of the classroom environment and the practitioner's office. This book not only gu