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Complex surveys
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 gathered from complex surveys to carry out traditional analyses. Complex Surveys is a practical guide to the analysis of this kind of data using R, the freely available and downloadable statistical programming language. As creator of the specific survey package for R, the author provides the ultimate presentation of how to successfully use the software for analyzing data from complex surveys while also utilizing the most current data from health and social sciences studies to demonstrate the application of survey research methods in these fields. The book begins with coverage of basic tools and topics within survey analysis such as simple and stratified sampling, cluster sampling, linear regression, and categorical data regression. Subsequent chapters delve into more technical aspects of complex survey analysis, including post-stratification, two-phase sampling, missing data, and causal inference. Throughout the book, an emphasis is placed on graphics, regression modeling, and two-phase designs. In addition, the author supplies a unique discussion of epidemiological two-phase designs as well as probability-weighting for causal inference. All of the book's examples and figures are generated using R, and a related Web site provides the R code that allows readers to reproduce the presented content. Each chapter concludes with exercises that vary in level of complexity, and detailed appendices outline additional mathematical and computational descriptions to assist readers with comparing results from various software systems. Complex Surveys is an excellent book for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. It is also a practical reference guide for applied statisticians and practitioners in the social and health sciences who use statistics in their everyday work.
eBook
Computational statistics and applications
Nature evolves mainly in a statistical way. Different strategies, formulas, and conformations are continuously confronted in the natural processes. Some of them are selected and then the evolution continues with a new loop of confrontation for the next generation of phenomena and living beings. Failings are corrected without a previous program or design. The new options generated by different statistical and random scenarios lead to solutions for surviving the present conditions. This is the general panorama for all scrutiny levels of the life cycles. Over three sections, this book examines different statistical questions and techniques in the context of machine learning and clustering methods, the frailty models used in survival analysis, and other studies of statistics applied to diverse problems.
Book
Advanced R statistical programming and data models : analysis, machine learning, and visualization
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 chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You'll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. You will: Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processing Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification Address missing data using multiple imputation in R Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability .
Book
An introduction to statistical computing : a simulation-based approach
A comprehensive introduction to sampling-based methods in statistical computing
The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models. This book gives a comprehensive introduction to the exciting area of sampling-based methods.
An Introduction to Statistical Computing introduces the classical topics of random number generation and Monte Carlo methods. It also includes some advanced methods such as the reversible jump Markov chain Monte Carlo algorithm and modern methods such as approximate Bayesian computation and multilevel Monte Carlo techniques
An Introduction to Statistical Computing:
* Fully covers the traditional topics of statistical computing.
* Discusses both practical aspects and the theoretical background.
* Includes a chapter about continuous-time models.
* Illustrates all methods using examples and exercises.
* Provides answers to the exercises (using the statistical computing environment R); the corresponding source code is available online.
* Includes an introduction to programming in R.
This book is mostly self-contained; the only prerequisites are basic knowledge of probability up to the law of large numbers. Careful presentation and examples make this book accessible to a wide range of students and suitable for self-study or as the basis of a taught course.
eBook
Course in Statistics with R
Integrates the theory and applications of statistics using R this book has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation.
eBook
Applied Psychometrics Using SAS
The book will be designed primarily for graduate students (or advanced undergraduates) who are learning psychometrics, as well as professionals in the field who need a reference for use in their practice. We would assume that users have some basic knowledge of using SAS to read data and conduct basic analyses (e.g., descriptive statistics, frequency distributions). In addition, the reader should be familiar with basic statistical concepts such as descriptive statistics (e.g., mean, median, variance, standard deviation), percentiles and the rudiments of hypothesis testing. They should also have a passing familiarity with issues in psychometrics such as reliability, validity and test/survey scoring. We will not assume any more than basic familiarity with these issues, and will devote a portion of each chapter (as well as the entire first chapter) to reviewing many of these basic ideas for those not familiar with them.We envision the book as being useful either as a primary text for a course on applied measurement where SAS is the main platform for instruction, or as a supplement to a more theoretical text. We also anticipate that readers working in government agencies responsible for testing and measurement issues at the local, state and national levels, and private testing, survey and market research companies, as well as faculty members needing a practical resource for psychometric practice will serve as a market for the book. In short, the readership would include graduate students, faculty members, data analysts and psychometricians responsible for analysis of survey response data, as well as educational and psychological assessments.The goal of the book is to provide readers with the tools necessary for assessing the psychometric qualities of educational and psychological measures as well as surveys and questionnaires. Each chapter will cover an issue pertinent to psychometric and measurement practice, with an emphasis on application. Topics will be briefly discussed from a theoretical/technical perspective in order to provide the reader with the background necessary to correctly use and interpret the statistical analyses that will be presented subsequently. Readers will then be presented with examples illustrating a particular concept (e.g., reliability). These examples will include a discussion of the particular analysis, along with the SAS code necessary to conduct them. The resulting output will then be discussed in detail, focusing on the interpretation of the results. Finally, examples of how these results might be written up will also be included in the text. It is hoped that this mixture of theory with examples of actual practice will serve the reader both as a pedagogical tool and as a reference work.
eBook