Practical Data Processing and Analysis Using R

Qualitative and Mixed Methods Data Analysis Using Dedoose®: A Practical Approach for Research Across the Social Sciences provides both new and experienced researchers with a guided introduction to dea

Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its s

Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapt

Almost 50 years have passed since the famous papers of Hugo Rietveld from the late sixties where he describes a method for the refinement of crystal structures from neutron powder diffraction data. So

An open and dynamic software for statistical analysis, R has become increasingly popular among students and researchers alike for its powerful language and graphical abilities. This book inc

This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data anal

Chromatin immunoprecipitation sequencing (ChIP-seq), which maps the genome-wide localization patterns of transcription factors and epigenetic marks, is among the most widely used methods in molecular

This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ research and from the public domain, a

Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human exper

The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathe

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 sci

This book focuses is on data science. It includes plenty of actual examples of the typical data processing and data presentations required of a professional data scientist. The material will be especi

This book focuses is on data science. It includes plenty of actual examples of the typical data processing and data presentations required of a professional data scientist. The material will be especi

This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation an

Biostatistics and Computer-based Analysis of Health Data Using SAS is primarily intended for health researchers who have basic knowledge in statistical methodology. It mainly focuses on the use of SAS

Electronic health records (EHRs) have become commonplace in the medical profession. Health data are readily captured and permanently stored in a digital fashion, and consequently, are increasingly bei

Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains

This book was developed to help students and researchers in the fields of economics, finance, law and other social science areas to understand and apply neuroscience. With the use of neuroscience tech

Biostatistics and Computer-Based Analysis of Health Data Using Stata is primarily intended for health researchers who have basic knowledge of statistical methodology and relies on the use of Stata to

This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research.The authors proceed from relatively basic concepts related to compu

Biclustering is a data-mining technique that allows simultaneous clustering of rows and columns within a matrix. This book focuses on biclustering as an unsupervised learning method for high-dimension

The special nature of discrete variables and frequency data vis-a-vis statistical graphics is now more widely accepted, and many of these methods (e.g., mosaic displays, fourfold plots, diagnostic plo

The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concep

This book is a comprehensive introduction to financial modeling that teaches advanced undergraduate and graduate students in finance and economics how to use R to analyze financial data and implement

Utilizing unique real-world examples and numerous graphics, this introductory book provides a solid foundation of the statistical methods that are used to analyze biomarker data and features software

A popular and efficient tool to analyze grouped data of a certain event, APC analysis is used in a range of application areas, including public health, social science, demography, and economics. This

This timely, thoughtful book provides a clear introduction to using panel data in research. It describes the different types of panel datasets commonly used for empirical analysis, and how to use them

This timely, thoughtful book provides a clear introduction to using panel data in research. It describes the different types of panel datasets commonly used for empirical analysis, and how to use them

Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-dev

This widely praised book shows how equivalent statistical methods can be applied in either SAS or R, enabling users of each software package to learn how to apply the methods in the other. The book co

The authors have led workshops on the subject for the past decade, sponsored each year by the American Psychological Association, and they have written this text in part as a means of encouraging the

Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative scie

This revised and updated edition focuses on constrained ordination (RDA, CCA), variation partitioning and the use of permutation tests of statistical hypotheses about multivariate data. Both classification and modern regression methods (GLM, GAM, loess) are reviewed and species functional traits and spatial structures analysed. Nine case studies of varying difficulty help to illustrate the suggested analytical methods, using the latest version of Canoco 5. All studies utilise descriptive and manipulative approaches, and are supported by data sets and project files available from the book website: http://regent.prf.jcu.cz/maed2/. Written primarily for community ecologists needing to analyse data resulting from field observations and experiments, this book is a valuable resource to students and researchers dealing with both simple and complex ecological problems, such as the variation of biotic communities with environmental conditions or their response to experimental manipulation.

Instead of presenting the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers, Using R for Numerical Analysis in Science and Engineering shows

"This book highlights the advantages of using data envelopment analysis as a tool to improve business performances and identifying the source of inefficiency in public and private organizations"--Prov