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Author: John H. Aldrich
Publisher: SAGE
Published: 1984-11
Total Pages: 100
ISBN-13: 9780803921337
DOWNLOAD EBOOKAfter showing why ordinary regression analysis is not appropriate for investigating dichotomous or otherwise 'limited' dependent variables, this volume examines three techniques which are well suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models.
Author: Tim Futing Liao
Publisher: SAGE
Published: 1994-06-30
Total Pages: 100
ISBN-13: 9780803949997
DOWNLOAD EBOOKWhat is the probability that something will occur, and how is that probability altered by a change in an independent variable? To answer these questions, Tim Futing Liao introduces a systematic way of interpreting commonly used probability models. Since much of what social scientists study is measured in noncontinuous ways and, therefore, cannot be analyzed using a classical regression model, it becomes necessary to model the likelihood that an event will occur. This book explores these models first by reviewing each probability model and then by presenting a systematic way for interpreting the results from each.
Author: Vani K. Borooah
Publisher: SAGE
Published: 2002
Total Pages: 108
ISBN-13: 9780761922421
DOWNLOAD EBOOKMany problems in the social sciences are amenable to analysis using the analytical tools of logit and probit models. This book explains what ordered and multinomial models are and also shows how to apply them to analysing issues in the social sciences.
Author: Alfred DeMaris
Publisher: SAGE
Published: 1992-06-06
Total Pages: 100
ISBN-13: 9780803943773
DOWNLOAD EBOOKLogit models : theoretical background. Logit models for multidimensional tables. Logistic regression. Advanced topics in logistic regression. Appendix : Computer routines.
Author: J. Scott Long
Publisher: SAGE
Published: 1997-01-09
Total Pages: 334
ISBN-13: 9780803973749
DOWNLOAD EBOOKEvaluates the most useful models for categorical and limited dependent variables (CLDVs), emphasizing the links among models and applying common methods of derivation, interpretation, and testing. The author also explains how models relate to linear regression models whenever possible. Annotation c.
Author: William H. Greene
Publisher: Cambridge University Press
Published: 2010-04-08
Total Pages: 383
ISBN-13: 1139485954
DOWNLOAD EBOOKIt is increasingly common for analysts to seek out the opinions of individuals and organizations using attitudinal scales such as degree of satisfaction or importance attached to an issue. Examples include levels of obesity, seriousness of a health condition, attitudes towards service levels, opinions on products, voting intentions, and the degree of clarity of contracts. Ordered choice models provide a relevant methodology for capturing the sources of influence that explain the choice made amongst a set of ordered alternatives. The methods have evolved to a level of sophistication that can allow for heterogeneity in the threshold parameters, in the explanatory variables (through random parameters), and in the decomposition of the residual variance. This book brings together contributions in ordered choice modeling from a number of disciplines, synthesizing developments over the last fifty years, and suggests useful extensions to account for the wide range of sources of influence on choice.
Author: Constantin Colonescu
Publisher: Lulu.com
Published: 2017-12-28
Total Pages: 278
ISBN-13: 1387473611
DOWNLOAD EBOOKThis is a beginner's guide to applied econometrics using the free statistics software R. It provides and explains R solutions to most of the examples in 'Principles of Econometrics' by Hill, Griffiths, and Lim, fourth edition. 'Using R for Principles of Econometrics' requires no previous knowledge in econometrics or R programming, but elementary notions of statistics are helpful.
Author: Kenneth Train
Publisher: Cambridge University Press
Published: 2009-07-06
Total Pages: 399
ISBN-13: 0521766559
DOWNLOAD EBOOKThis book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Author: Ronald Christensen
Publisher: Springer Science & Business Media
Published: 2006-04-06
Total Pages: 498
ISBN-13: 0387226249
DOWNLOAD EBOOKThe primary focus here is on log-linear models for contingency tables, but in this second edition, greater emphasis has been placed on logistic regression. The book explores topics such as logistic discrimination and generalised linear models, and builds upon the relationships between these basic models for continuous data and the analogous log-linear and logistic regression models for discrete data. It also carefully examines the differences in model interpretations and evaluations that occur due to the discrete nature of the data. Sample commands are given for analyses in SAS, BMFP, and GLIM, while numerous data sets from fields as diverse as engineering, education, sociology, and medicine are used to illustrate procedures and provide exercises. Throughoutthe book, the treatment is designed for students with prior knowledge of analysis of variance and regression.
Author: Jason W. Osborne
Publisher: SAGE Publications
Published: 2014-02-26
Total Pages: 489
ISBN-13: 1483312097
DOWNLOAD EBOOKJason W. Osborne’s Best Practices in Logistic Regression provides students with an accessible, applied approach that communicates logistic regression in clear and concise terms. The book effectively leverages readers’ basic intuitive understanding of simple and multiple regression to guide them into a sophisticated mastery of logistic regression. Osborne’s applied approach offers students and instructors a clear perspective, elucidated through practical and engaging tools that encourage student comprehension.
