Statistics: Starting out

Practical and up-to-date, Structural Equation Modeling includes chapters on major aspects of the structural equation modeling approach to research design and data analysis. The chapters, written by internationally recognized leaders in the development and application of structural equation modeling, target graduate students and seasoned researchers in the social and behavioral sciences who wish to understand the basic concepts and issues associated with the structural equation modeling approach as well as potential applications to research problems. Although technically sound, the chapters are primarily nontechnical in content and style, a characteristic that makes the volume an excellent introduction to the structural equation modeling approach for readers who have a firm grasp on traditional inferential statistics.

Since the publication of the Second Edition of this popular textbook, new standards have changed the focus of reliability engineering, which introduced new concepts and terminology. Consequently, the Third Edition of System Reliability Theory: Models, Statistical Methods, and Applications has been thoroughly rewritten and updated to meet current standards. With an updated practical focus, incorporation of industry feedback, and many new examples based on real-world industry problems and data, this book begins with an introduction on reliability engineering and is followed by coverage on failures and failure analysis.

Its main objective is to examine the application and relevance of Bayes'theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.

Science has never been more important, yet science education faces serious challenges. At present, science education research only sees half the picture, focusing on how students learn and their changing conceptions. Both teaching practice and what is taught, science knowledge itself, are missing. This book offers new, interdisciplinary ways of thinking about science teaching that foreground the forms taken by science knowledge and the language, imagery and gesture through which they are expressed. This book brings together leading international scholars from Systemic Functional Linguistics, a long-established approach to language, and Legitimation Code Theory, a rapidly growing sociological approach to knowledge practices.

The revised and enlarged edition of ‘Fundamentals of Biostatistics’ will be found increasingly useful with additional information and a large number of solved examples. The chapter on the use of Antilogarithm has been removed keeping in view the high standard of students of Biostatistics.

A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models.

The first book to provide a unified framework for both single-level and multilevel modeling of ordinal categorical data, Applied Ordinal Logistic Regression Using Stata by Xing Liu helps readers learn how to conduct analyses, interpret the results from Stata output, and present those results in scholarly writing. Using step-by-step instructions, this non-technical, applied book leads students, applied researchers, and practitioners to a deeper understanding of statistical concepts by closely connecting the underlying theories of models with the application of real-world data using statistical software.

The fourth edition of Generalized Linear Models and Extensions gives a comprehensive overview of the nature and scope of generalized linear models (GLMs) and of the major changes to the basic GLM algorithm that allow modeling of data that violate GLM distributional assumptions. The text stands out in its coverage of the derivation of the GLM families and their foremost links, but it also guides the reader in applying the various models to real data. This edition has new sections on bivariate and multivariate models including bivariate count data models estimated via copula functions and models based on bivariate distributions put forward by Famoye and by Marshall and Olkin. In addition, there are new sections on Bayesian GLMs illustrating background, the estimation of models using the bayesmh command of Stata 14, and the updated bayes prefix syntax available in Stata 15.