Download or read book Linear Probability Logit and Probit Models written by John H. Aldrich and published by SAGE. This book was released on 1984-11 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: After 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.
Download or read book Interpreting Probability Models written by Tim Futing Liao and published by SAGE. This book was released on 1994-06-30 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: What 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.
Download or read book Logit and Probit written by Vani K. Borooah and published by SAGE. This book was released on 2002 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many 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.
Download or read book Linear probability logit and probit models By John H Aldrich and Forrest D Nelson written by John H. Aldrich and published by . This book was released on 1984 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Linear Probability Logit and Probit Models written by John H. Aldrich and published by SAGE Publications, Incorporated. This book was released on 1984-11-01 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ordinary regression analysis is not appropriate for investigating dichotomous or otherwise "limited" dependent variables, but this volume examines three techniques -- linear probability, probit, and logit models -- which are well-suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models. Using detailed examples, Aldrich and Nelson point out the differences among linear, logit, and probit models, and explain the assumptions associated with each.
Download or read book Regression Models for Categorical and Limited Dependent Variables written by J. Scott Long and published by SAGE. This book was released on 1997-01-09 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evaluates 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.
Download or read book Modeling Ordered Choices written by William H. Greene and published by Cambridge University Press. This book was released on 2010-04-08 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: It 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.
Download or read book Logit Modeling written by Alfred DeMaris and published by SAGE. This book was released on 1992-06-06 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Logit models : theoretical background. Logit models for multidimensional tables. Logistic regression. Advanced topics in logistic regression. Appendix : Computer routines.
Download or read book Using R for Principles of Econometrics written by Constantin Colonescu and published by Lulu.com. This book was released on 2017-12-28 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 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.
Download or read book Discrete Choice Methods with Simulation written by Kenneth Train and published by Cambridge University Press. This book was released on 2009-07-06 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 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.
Download or read book Log Linear Models and Logistic Regression written by Ronald Christensen and published by Springer Science & Business Media. This book was released on 2006-04-06 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 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.
Download or read book Logit and Probit written by Vani K. Borooah and published by SAGE. This book was released on 2002 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many 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.
Download or read book Best Practices in Logistic Regression written by Jason W. Osborne and published by SAGE Publications. This book was released on 2014-02-26 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: Jason 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.
Download or read book Logit Models from Economics and Other Fields written by J. S. Cramer and published by Cambridge University Press. This book was released on 2003-08-21 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Logistic models are widely used in economics and other disciplines and are easily available as part of many statistical software packages. This text for graduates, practitioners and researchers in economics, medicine and statistics, which was originally published in 2003, explains the theory underlying logit analysis and gives a thorough explanation of the technique of estimation. The author has provided many empirical applications as illustrations and worked examples. A large data set - drawn from Dutch car ownership statistics - is provided online for readers to practise the techniques they have learned. Several varieties of logit model have been developed independently in various branches of biology, medicine and other disciplines. This book takes its inspiration from logit analysis as it is practised in economics, but it also pays due attention to developments in these other fields.
Download or read book Econometrics For Dummies written by Roberto Pedace and published by John Wiley & Sons. This book was released on 2013-06-05 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Score your highest in econometrics? Easy. Econometrics can prove challenging for many students unfamiliar with the terms and concepts discussed in a typical econometrics course. Econometrics For Dummies eliminates that confusion with easy-to-understand explanations of important topics in the study of economics. Econometrics For Dummies breaks down this complex subject and provides you with an easy-to-follow course supplement to further refine your understanding of how econometrics works and how it can be applied in real-world situations. An excellent resource for anyone participating in a college or graduate level econometrics course Provides you with an easy-to-follow introduction to the techniques and applications of econometrics Helps you score high on exam day If you're seeking a degree in economics and looking for a plain-English guide to this often-intimidating course, Econometrics For Dummies has you covered.
Download or read book Linear Probability Models LPM and Big Data written by Suneel Chatla and published by . This book was released on 2016 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear regression is among the most popular statistical models in social sciences research. Linear probability models (LPMs) - linear regression models applied to a binary outcome - are used in various disciplines. Surprisingly, LPMs are rare in the IS literature, where logit and probit models are typically used for binary outcomes. LPMs have been examined with respect to specific aspects, but a thorough evaluation of their practical pros and cons for different research goals under different scenarios is missing. We perform an extensive simulation study to evaluate the advantages and dangers of LPMs, especially in the realm of Big Data that now affects IS research. We evaluate LPM for the three common uses of binary outcome models: inference and estimation, prediction and classification, and selection bias. We compare its performance to logit and probit, under different sample sizes, error distributions, and more. We find that coefficient directions, statistical significance, and marginal effects yield results similar to logit and probit. Although LPM coefficients are biased, they are consistent for the true parameters up to a multiplicative scalar. Coefficient bias can be corrected by assuming an error distribution. For classification and selection bias, LPM is on par with logit and probit in terms of class separation and ranking, and is a viable alternative in selection models. It is lacking when the predicted probabilities are directly of interest, because predicted probabilities can exceed the unit interval. We illustrate some of these results through by modeling price in online auctions, using data from eBay.
Download or read book Logit and Probit written by Andrew S. Fullerton and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Logit and probit are regression models for binary outcomes that allow one to avoid the problems associated with the linear probability model, such as nonconstant error variance and the unrealistic assumption of linearity in the parameters. Logit and probit also serve as building blocks for more advanced regression models for other categorical outcomes. In this entry, the focus is on logit and probit models for binary and nominal outcomes. Binary outcomes are dichotomous-dependent variables coded as 0 or 1. Nominal outcomes are dependent variables with three or more unordered categories. The entry considers several topics related to binary and multinomial logit/probit models, including motivation for the models, estimation, interpretation, hypothesis testing, model assumptions, and connections to ordered regression models. The features of the models are illustrated with examples using Stata.
