Spatial Data Analysis

An updated treatment of categorical data analysis in the biomedical sciences that now explores applications to translational researchThoroughly updated with the latest advances in the field, Applied C

This book helps readers prepare and successfully finish qualitative data analysis. It sets out an easy to grasp model-the spiral of analysis-that shows how to break up the data with coding and how to

This book helps readers prepare and successfully finish qualitative data analysis. It sets out an easy to grasp model-the spiral of analysis-that shows how to break up the data with coding and how to

Clinical Data-Mining (CDM) involves the conceptualization, extraction, analysis, and interpretation of available clinical data for practice knowledge-building, clinical decision-making and practition

Data compression is one of the most important fields and tools in modern computing. From archiving data, to CD-ROMs, and from coding theory to image analysis, many facets of modern computing rely upon

Together with the development of information technologies the requirements for data analysis tools have grown significantly. Due to such recent advances, as ubiquitous computing or the Internet of thi

Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widesp

Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widesp

The second edition of Statistics and Data Analysis for Nursing , uses a conversational style to teach students how to use statistical methods and procedures to analyze research findings. Readers ar

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

Interest in the geography of economic and social change has grown in Britain over recent years as people have become more aware of regional imbalances and as government influence on the evolution of the country's spatial systems has increased. The processes of spatial allocation are becoming better understood as new techniques of analysis are developed. But weaknesses and inconsistencies in public policies remain and have attracted the attention of workers in various disciplines, particularly geographers. This awareness is reflected in these essays, most of which deal with economic rather than the social problems. They demonstrate the wide range of issues on which geographers can make a relevant contribution.

Presents a comprehensive treatment of linear mixed models for continuous longitudinal data. This book puts major emphasis on exploratory data analysis for all aspects of the model, such as the margina

Through simple, practical approaches, Reliability Analysis and Prediction with Warranty Data: Issues, Strategies, and Methods helps Six Sigma black belts and engineers successfully interpret warranty

Data flow analysis is used to discover information for a wide variety of useful applications, ranging from compiler optimizations to software engineering and verification. Modern compilers apply it to

This book provides an up-to-date treatment of the foundations common to the statistical analysis of network data across the disciplines. The material is organized according to a statistical taxonomy,

This book provides a clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. Meta-analysis has become a critically important tool in field

Roxy Peck, Chris Olsen and Jay Devore's new edition uses real data and attention-grabbing examples to introduce users to the study of statistical output and methods of data analysis. Based on the best

This book is an accessible introduction to quantitative data analysis, concentrating on the key issues facing those new to research, such as how to decide which statistical procedure is suitable, and

Most of modern enterprises, institutions, and organizations rely on knowledge-based management systems. In these systems, knowledge is gained from data analysis. Nowadays, knowledge-based management

This book provides a practical, comprehensive and up-to-date overview of the use of spatial statistics in epidemiology - the study of the incidence and distribution of diseases. Used appropriately, s

Unlike other books on the modeling and analysis of experimental data, Design and Analysis of Experiments: Classical and Regression Approaches with SAS not only covers classical experimental design the

The book provides an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. The book starts with a presentation of

Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data display

"This is an ideal introductory book for budding researchers who are embarking on the development and then analysis of data, and in this case, more specifically questionnaires using partly or exclusive

Few books on statistical data analysis in the natural sciences are written at a level that a non-statistician will easily understand. This is a book written in colloquial language, avoiding mathemati

This book consists of surveys of high-frequency financial data analysis and econometric forecasting. Some of the chapters were presented as tutorials to an audience in the Econometric Forecasting and

This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in

This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in

Drawing from the authors’ own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a

Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of ver

Praise for the First Edition "This is a superb text from which to teach categorical data analysis, at a variety of levels. . . [t]his book can be very highly recommended."—Short Book Reviews "Of great

A must-have volume for every communication researcher's library, The SAGE Sourcebook of Advanced Data Analysis Methods for Communication Research provides an introductory treatment of various advance

Successful statistical analysis of research data is an integral part of any social sciences degree. This straightforward and accessible text provides students with both a beginners guide and a continu

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

Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use

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

In this new volume, renowned authors contribute fascinating, cutting-edge insights into microarray data analysis. Information on an array of topics is included in this innovative book including in-dep

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.

Here is an in-depth guide to the most powerful available benchmarking technique for improving service organization performance — Data Envelopment Analysis (DEA). The book outlines DEA as a benchmarkin