Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Since the linear model forms the groundwork for most applied statistics, a course on the theory of the linear model is often required in most graduate statistics programs. A Primer on Linear Models presents a concise yet complete foundation for understanding basic linear models. Designed for a one-semester graduate course, this textbook begins with a practical discussion of basic algebra and geometry concepts as they apply to the linear model. The book then proceeds to an in-depth treatment of more advanced topics such as the Gauss-Markov model. The text also includes exercises of various levels of difficulty and features the constant use of non full-rank design matrices.

Linear models (Statistics). --- Linear models (Statistics)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

"Over the past ten years, there have been many important advances in log-linear modeling, including the specification of new models, in particular non-standard models, and their relationships to methods such as Rasch modeling. While most literature on the topic is contained in volumes aimed at advanced statisticians, Applied Log-Linear Modeling presents the topic in an accessible style that is customized for applied researchers who utilize log-linear modeling in the social sciences. The book begins by providing readers with a foundation on the basics of log-linear modeling, introducing decomposing effects in cross-tabulations and goodness-of-fit tests. Popular hierarchical log-linear models are illustrated using empirical data examples, and odds ratio analysis is discussed as an interesting method of analysis of cross-tabulations. Next, readers are introduced to the design matrix approach to log-linear modeling, presenting various forms of coding (effects coding, dummy coding, Helmert contrasts etc.) and the characteristics of design matrices. The book goes on to explore non-hierarchical and nonstandard log-linear models, outlining ten nonstandard log-linear models (including nonstandard nested models, models with quantitative factors, logit models, and log-linear Rasch models) as well as special topics and applications. A brief discussion of sampling schemes is also provided along with a selection of useful methods of chi-square decomposition. Additional topics of coverage include models of marginal homogeneity, rater agreement, methods to test hypotheses about differences in associations across subgroup, the relationship between log-linear modeling to logistic regression, and reduced designs. Throughout the book, Computer Applications chapters feature SYSTAT, Lem, and R illustrations of the previous chapter's material, utilizing empirical data examples to demonstrate the relevance of the topics in modern research"

Log-linear models --- Mathematics

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Matrices. --- Linear models (Statistics).

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

"Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the generalized linear mixed model (GLMM) as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider. Along with describing common applications of GLMMs, the text introduces the essential theory and main methodology associated with linear models that accommodate random model effects and non-Gaussian data. Unlike traditional linear model textbooks that focus on normally distributed data, this one adopts a generalized mixed model approach throughout: data for linear modeling need not be normally distributed and effects may be fixed or random. With numerous examples using SAS® PROC GLIMMIX, this book is ideal for graduate students in statistics, statistics professionals seeking to update their knowledge, and researchers new to the generalized linear model thought process. It focuses on data-driven processes and provides context for extending traditional linear model thinking to generalized linear mixed modeling"--

Linear models (Statistics)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This volume explains the theoretical underpinnings of this type of statistical analysis. It shows how to decide how to select the best way to adapt data to generalized linear models and provides examples throughout.

Linear models (Statistics)