Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Applied MANOVA and Discriminant Analysis
A complete introduction to discriminant analysis--extensively revised, expanded, and updated This Second Edition of the classic book, Applied Discriminant Analysis, reflects and references current usage with its new title, Applied MANOVA and Discriminant Analysis. Thoroughly updated and revised, this book continues to be essential for any researcher or student needing to learn to speak, read, and write about discriminant analysis as well as develop a philosophy of empirical research and data analysis. Its thorough introduction to the application of discriminant analysis is unparalleled. Offering the most up-to-date computer applications, references, terms, and real-life research examples, th...
Applied Discriminant Analysis
Most books on discriminant analysis focus on statistical theory. But properly applied, discriminant analysis methods can be enormously useful in the interpretation of data. This book is the first ever to offer a complete introduction to discriminant analysis that focuses on applications. It provides numerous examples, explained in great detail, using current statistical discriminant analysis algorithms. It also develops several themes that will be useful to researchers and students regardless of the analytical methods they employ. They are the careful examination of data prior to final analysis; the application of critical judgment and common sense to all analyses and interpretations; and co...
Handbook of Applied Multivariate Statistics and Mathematical Modeling
Multivariate statistics and mathematical models provide flexible and powerful tools essential in most disciplines. Nevertheless, many practicing researchers lack an adequate knowledge of these techniques, or did once know the techniques, but have not been able to keep abreast of new developments. The Handbook of Applied Multivariate Statistics and Mathematical Modeling explains the appropriate uses of multivariate procedures and mathematical modeling techniques, and prescribe practices that enable applied researchers to use these procedures effectively without needing to concern themselves with the mathematical basis. The Handbook emphasizes using models and statistics as tools. The objective of the book is to inform readers about which tool to use to accomplish which task. Each chapter begins with a discussion of what kinds of questions a particular technique can and cannot answer. As multivariate statistics and modeling techniques are useful across disciplines, these examples include issues of concern in biological and social sciences as well as the humanities.
Higher Education: Handbook of Theory and Research
This handbook series provides a compendium of literature reviews on a wide array of topics of interest to the higher education scholarly and policy communities. Each chapter provides a comprehensive review of research findings on a selected topic, critiques the research literature, and sets forth an agenda for future research. Coverage focuses on twelve general areas that encompass the salient dimensions of scholarly and policy inquiries undertaken in the international community.
Higher Education: Handbook of Theory and Research
Published annually since 1985, the Handbook series provides an authoritative compendium of thorough and integrative literature reviews on a diverse array of topics of interest to the higher education scholarly and policy communities.
Higher Education: Handbook of Theory and Research 13
Published annually since 1985, the Handbook series provides a compendium of thorough and integrative literature reviews on a diverse array of topics of interest to the higher education scholarly and policy communities.
Multivariate General Linear Models
This title provides an integrated introduction to multivariate multiple regression analysis (MMR) and multivariate analysis of variance (MANOVA). It defines the key steps in analyzing linear model data and introduces multivariate linear model analysis as a generalization of the univariate model. Richard F. Haase focuses on multivariate measures of association for four common multivariate test statistics, presents a flexible method for testing hypotheses on models, and emphasizes the multivariate procedures attributable to Wilks, Pillai, Hotelling, and Roy.
