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An Introduction To Generalized Linear Models

Author: George H. Dunteman
Publisher: SAGE
ISBN: 9780761920847
Size: 52.17 MB
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Do you have data that is not normally distributed and don't know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors extend these concepts to GLM and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets. The book provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation; includes discussion on checking model adequacy and description on how to use SAS to fit GLM; and describes the connection between survival analysis and GLM. It is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis.

Modern Methods For Robust Regression

Author: Robert Andersen
Publisher: SAGE
ISBN: 1412940729
Size: 26.90 MB
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Geared towards both future and practising social scientists, this book takes an applied approach and offers readers empirical examples to illustrate key concepts. It includes: applied coverage of a topic that has traditionally been discussed from a theoretical standpoint; empirical examples to illustrate key concepts; a web appendix that provides readers with the data and the R-code for the examples used in the book.

The Association Graph And The Multigraph For Loglinear Models

Author: Harry J. Khamis
Publisher: SAGE
ISBN: 1452238952
Size: 35.90 MB
Format: PDF, Kindle
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The Association Graph and the Multigraph for Loglinear Models will help students, particularly those studying the analysis of categorical data, to develop the ability to evaluate and unravel even the most complex loglinear models without heavy calculations or statistical software. This supplemental text reviews loglinear models, explains the association graph, and introduces the multigraph to students who may have little prior experience of graphical techniques, but have some familiarity with categorical variable modeling. The author presents logical step-by-step techniques from the point of view of the practitioner, focusing on how the technique is applied to contingency table data and how the results are interpreted.

An R And S Plus Companion To Applied Regression

Author: John Fox
Publisher: SAGE
ISBN: 9780761922803
Size: 53.31 MB
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"This book fits right into a needed niche: rigorous enough to give full explanation of the power of the S language, yet accessible enough to assign to social science graduate students without fear of intimidation. It is a tremendous balance of applied statistical "firepower" and thoughtful explanation. It meets all of the important mechanical needs: each example is given in detail, code and data are freely available, and the nuances of models are given rather than just the bare essentials. It also meets some important theoretical needs: linear models, categorical data analysis, an introduction to applying GLMs, a discussion of model diagnostics, and useful instructions on writing customized functions. " —JEFF GILL, University of Florida, Gainesville

Logistic Regression Models For Ordinal Response Variables

Author: Ann A. O'Connell
Publisher: SAGE Publications, Incorporated
ISBN: 9780761929895
Size: 79.88 MB
Format: PDF, Mobi
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This concise guide provides applied researchers in the fields of the social, educational and behavioural sciences with comprehensive and accessible coverage of the different analyses for ordinal outcomes of the logistic regression models.

Dynamic Modeling

Author: R. Robert Huckfeldt
Publisher: SAGE
ISBN: 9780803909465
Size: 63.99 MB
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Outlines the theory behind, and techniques for, using dynamic modeling, taking the reader through a series of increasingly complex models. At each step, examples are used to claify applications of different equation models.Learn more about "The Little Green Book" - QASS Series! Click Here