Practical Data Processing and Analysis Using R

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

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

Bridging the gap between introductory theory and practical knowledge, this second edition reflects the fast-moving field of DNA microarrays by adding new and updated chapters that cover cutting-edge m

This book introduces the statistical software R to the image processing community in an intuitive and practical manner. R brings interesting statistical and graphical tools which are important and nec

In this important new text, Paul Sullivan introduces readers to a qualitative methodology rooted in the analysis of dialogue and subjectivity: the dialogical approach. Sullivan unpacks the theory behi

Vegetation Description and Data Analysis: A Practical Approach, Second Edition is a fully revised and up-dated edition of this key text. The book takes account of recent advances in the field whilst r

Vegetation Description and Data Analysis: A Practical Approach, Second Edition is a fully revised and up-dated edition of this key text. The book takes account of recent advances in the field whilst r

Blinowska-Cieslak and Zygierewicz (both biomedical physics, U. of Warsaw, Poland) offer a guide for graduate students, researchers early in their career, commercial researchers, and others engaged wit

This book describes methods and software implementations for the analysis of interval-censored data. The authors present the theoretical background for all methods and apply the methods to real data s

A practical guide to selecting and applying the most appropriate model for analysis of cross section data using EViews."This book is a reflection of the vast experience and knowledge of the author. It

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most s

Information systems and intelligent knowledge processing are playing an increasing role in business, science and technology. Recently, advanced information systems have evolved to facilitate the co-e

A unique companion for statistical coders, Using SAS for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in SAS, without having to

Using simple language and illustrative examples, this book comprehensively covers data management tasks that bridge the gap between raw data and statistical analysis. Rather than focus on clusters of

The advances of book history and editorial theory remind us that it is vital to look behind the text we read. Sukanta Chaudhuri explores, at a very fundamental level, how texts are constituted and ho

Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, data-based introduction to methods of survival analysis. Research

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 gat

This book presents the basic procedures for utilizing SAS Enterprise Guide to analyze statistical data. SAS Enterprise Guide is a graphical user interface (point and click) to the main SAS application. Each chapter contains a brief conceptual overview and then guides the reader through concrete step-by-step examples to complete the analyses. The eleven sections of the book cover a wide range of statistical procedures including descriptive statistics, correlation and simple regression, t tests, one-way chi square, data transformations, multiple regression, analysis of variance, analysis of covariance, multivariate analysis of variance, factor analysis, and canonical correlation analysis. Designed to be used either as a stand-alone resource or as an accompaniment to a statistics course, the book offers a smooth path to statistical analysis with SAS Enterprise Guide for advanced undergraduate and beginning graduate students, as well as professionals in psychology, education, business, hea

This book presents the basic procedures for utilizing SAS Enterprise Guide to analyze statistical data. SAS Enterprise Guide is a graphical user interface (point and click) to the main SAS application. Each chapter contains a brief conceptual overview and then guides the reader through concrete step-by-step examples to complete the analyses. The eleven sections of the book cover a wide range of statistical procedures including descriptive statistics, correlation and simple regression, t tests, one-way chi square, data transformations, multiple regression, analysis of variance, analysis of covariance, multivariate analysis of variance, factor analysis, and canonical correlation analysis. Designed to be used either as a stand-alone resource or as an accompaniment to a statistics course, the book offers a smooth path to statistical analysis with SAS Enterprise Guide for advanced undergraduate and beginning graduate students, as well as professionals in psychology, education, business, hea

An All-in-One Resource for Using SAS and R to Carry out Common TasksProvides a path between languages that is easier than reading complete documentationSAS and R: Data Management, Statistical Analysis

Do you want to recognize the most suitable models for analysis of statistical data sets? This book provides a hands-on practical guide to using the most suitable models for analysis of statistical da

The Workflow of Data Analysis Using Stata, by J. Scott Long, is an essential productivity tool for data analysts. Long presents lessons gained from his experience and demonstrates how to design and im

This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapters include clustering, supervised classification, and working with m

The most thorough and up-to-date introduction to data mining techniques using SAS Enterprise Miner. The Sample, Explore, Modify, Model, and Assess (SEMMA) methodology of SAS Enterprise Miner is an ex

Previously known as Statistical Methods for Health Sciences, this bestselling resource is one of the first books to discuss the methodologies used for the analysis of clustered and correlated data. Wh

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

This book uses a mathematical approach to deriving the laws of science and technology, based upon the concept of Fisher information. The approach that follows from these ideas is called the principle

The preferred method of data analysis of quantitative experiments is the method of least squares. Often, however, the full power of the method is overlooked, and very few books deal with these techniq

Statistical analysis is ubiquitous in modern medical research. Logistic regression, generalized linear models, random effects models, and Cox's regression all have become commonplace in the medical li

Developed by Jean-Paul Benzerci more than 30 years ago, correspondence analysis as a framework for analyzing data quickly found widespread popularity in Europe. The topicality and importance of corres

This book is primarily written for students and researchers dealing with complex ecological problems. Multivariate statistical methods are described, and advice is given on how best to apply these met