Applied Logistic Regression Analysis

Author: Scott Menard

Publisher: SAGE

ISBN: 9780761922087

Category: Mathematics

Page: 111

View: 8993

DOWNLOAD NOW »

The focus in this Second Edition is on logistic regression models for individual level (but aggregate or grouped) data. Multiple cases for each possible combination of values of the predictors are considered in detail and examples using SAS and SPSS included. New to this edition: · More detailed consideration of grouped as opposed to casewise data throughout the book · Updated discussion of the properties and appropriate use of goodness of fit measures, R2 analogues, and indices of predictive efficiency · Discussion of the misuse of odds ratios to represent risk ratios, and of overdispersion and underdispersion for grouped data · Updated coverage of unordered and ordered polytomous logistic regression models.
Release

Logistic Regression

A Primer

Author: Fred C. Pampel

Publisher: SAGE

ISBN: 9780761920106

Category: Mathematics

Page: 86

View: 3610

DOWNLOAD NOW »

Pampel's book offers readers the first "nuts and bolts" approach to doing logistic regression through the use of careful explanations and worked-out examples. This book will enable readers to use and understand logistic regression techniques and will serve as a foundation for more advanced treatments of the topic.
Release

Regression Modeling Strategies

With Applications to Linear Models, Logistic Regression, and Survival Analysis

Author: Frank E. Harrell

Publisher: Springer Science & Business Media

ISBN: 147573462X

Category: Mathematics

Page: 572

View: 1233

DOWNLOAD NOW »

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".
Release

Multiple Regression

A Primer

Author: Paul D. Allison

Publisher: Pine Forge Press

ISBN: 9780761985334

Category: Mathematics

Page: 202

View: 4404

DOWNLOAD NOW »

Presenting topics in the form of questions and answers, this popular supplemental text offers a brief introduction on multiple regression on a conceptual level. Author Paul D. Allison answers the most essential questions (such as how to read and interpret multiple regression tables and how to critique multiple regression results) in the early chapters, and then tackles the less important ones (for instance, those arising from multicollinearity) in the later chapters. With this organization, readers can stop at the end of any chapter and still feel like they've already gotten the meat of the subject.
Release

Logistic Regression Models for Ordinal Response Variables

Author: Ann A. O'Connell

Publisher: SAGE

ISBN: 9780761929895

Category: Mathematics

Page: 107

View: 9017

DOWNLOAD NOW »

Logistic Regression Models for Ordinal Response Variables provides applied researchers in the social, educational, and behavioral sciences with an accessible and comprehensive coverage of analyses for ordinal outcomes. The content builds on a review of logistic regression, and extends to details of the cumulative (proportional) odds, continuation ratio, and adjacent category models for ordinal data. Description and examples of partial proportional odds models are also provided. This book is highly readable, with lots of examples and in-depth explanations and interpretations of model characteristics.
Release

Correlation and Regression Analysis

A Historian's Guide

Author: Thomas J. Archdeacon

Publisher: Univ of Wisconsin Press

ISBN: 9780299136543

Category: History

Page: 352

View: 2370

DOWNLOAD NOW »

In Correlation and Regression Analysis: A Historian's Guide Thomas J. Archdeacon provides historians with a practical introduction to the use of correlation and regression analysis. The book concentrates on the kinds of analysis that form the broad range of statistical methods used in the social sciences. It enables historians to understand and to evaluate critically the quantitative analyses that they are likely to encounter in journal literature and monographs reporting research findings in the social sciences. Without attempting to be a text in basic statistics, the book provides enough background information to allow readers to grasp the essentials of correlation and regression. Correlation analysis refers to the measurement of association between or among variables, and regression analysis focuses primarily on the use of linear models to predict changes in the value taken by one variable in terms of changes in the values of a set of explanatory variables. The book also discusses diagnostic methods for identifying shortcomings in regression models, the use of regression to analyze causation, and the application of regression and related procedures to the study of problems containing categorical as well as numerical data. Archdeacon asserts that knowing how statistical procedures are computed can clarify the theoretical structures underlying them and is essential for recognizing the conditions under which their use is appropriate. The book does not shy away from the mathematics of statistical analysis; but Archdeacon presents concepts carefully and explains the operation of equations step by step. Unlike many works in the field, the book does not assume that readers have mathematical training beyond basic algebra and geometry. In the hope of promoting the role of quantitative analysis in his discipline, Archdeacon discusses the theory and methods behind the most important interpretive paradigm for quantitative research in the social sciences. Correlation and Regression Analysis introduces statistical techniques that are indispensable to historians and enhances the presentation of them with practical examples from scholarly works.
Release

Interaction Effects in Logistic Regression

Author: James Jaccard,Jim Jaccard

Publisher: SAGE

ISBN: 9780761922070

Category: Social Science

Page: 70

View: 332

DOWNLOAD NOW »

This book provides an introduction to the analysis of interaction effects in logistic regression by focusing on the interpretation of the coefficients of interactive logistic models for a wide range of situations encountered in the research literature. The volume is oriented toward the applied researcher with a rudimentary background in multiple regression and logistic regression and does not include complex formulas that could be intimidating to the applied researcher.
Release

Regression With Social Data

Modeling Continuous and Limited Response Variables

Author: Alfred DeMaris

Publisher: John Wiley & Sons

ISBN: 9780471677550

Category: Mathematics

Page: 560

View: 2520

DOWNLOAD NOW »

An accessible introduction to the use of regression analysis inthe social sciences Regression with Social Data: Modeling Continuous and LimitedResponse Variables represents the most complete and fullyintegrated coverage of regression modeling currently available forgraduate-level behavioral science students and practitioners.Covering techniques that span the full spectrum of levels ofmeasurement for both continuous and limited response variables, andusing examples taken from such disciplines as sociology,psychology, political science, and public health, the authorsucceeds in demystifying an academically rigorous subject andmaking it accessible to a wider audience. Content includes coverage of: Logit, probit, scobit, truncated, and censored regressions Multiple regression with ANOVA and ANCOVA models Binary and multinomial response models Poisson, negative binomial, and other regression models forevent-count data Survival analysis using multistate, multiepisode, andinterval-censored survival models Concepts are reinforced throughout with numerous chapterproblems, exercises, and real data sets. Step-by-step solutionsplus an appendix of mathematical tutorials make even complexproblems accessible to readers with only moderate math skills. Thebook’s logical flow, wide applicability, and uniquelycomprehensive coverage make it both an ideal text for a variety ofgraduate course settings and a useful reference for practicingresearchers in the field.
Release