Logit And Probit
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Logit And Probit
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Author : Vani K. Borooah
language : en
Publisher: SAGE
Release Date : 2002
Logit And Probit written by Vani K. Borooah and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Mathematics categories.
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.
Linear Probability Logit And Probit Models
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Author : John H. Aldrich
language : en
Publisher: SAGE
Release Date : 1984-11
Linear Probability Logit And Probit Models written by John H. Aldrich and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984-11 with Mathematics categories.
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.
Logit And Probit
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Author : Andrew S. Fullerton
language : en
Publisher:
Release Date : 2020
Logit And Probit written by Andrew S. Fullerton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Economics categories.
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.
Interpreting Probability Models
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Author : Tim Futing Liao
language : en
Publisher: SAGE
Release Date : 1994-06-30
Interpreting Probability Models written by Tim Futing Liao and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-06-30 with Mathematics categories.
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.
Interpreting And Comparing Effects In Logistic Probit And Logit Regression
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Author : Jacques A. P. Hagenaars
language : en
Publisher: SAGE Publications
Release Date : 2024-01-16
Interpreting And Comparing Effects In Logistic Probit And Logit Regression written by Jacques A. P. Hagenaars and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-16 with Social Science categories.
Log-linear, logit and logistic regression models are the most common ways of analyzing data when (at least) the dependent variable is categorical. This volume shows how to compare coefficient estimates from regression models for categorical dependent variables in three typical research situations: (i) within one equation, (ii) between identical equations estimated in different subgroups, and (iii) between nested equations. Each of these three kinds of comparisons brings along its own particular form of comparison problems. Further, in all three areas, the precise nature of comparison problems in logistic regression depends on how the logistic regression model is looked at and how the effects of the independent variables are computed. This volume presents a practical, unified treatment of these problems, and considers the advantages and disadvantages of each approach, and when to use them, so that applied researchers can make the best choice related to their research problem. The techniques are illustrated with data from simulation experiments and from publicly available surveys. The datasets, along with Stata syntax, are available on a companion website at: https://study.sagepub.com/researchmethods/qass/hagenaars-interpreting-effects.
Comparing Regression Coefficients Between Models Using Logit And Probit
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Author : Kristian Bernt Karlson
language : en
Publisher:
Release Date : 2012
Comparing Regression Coefficients Between Models Using Logit And Probit written by Kristian Bernt Karlson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Logit and probit models are widely used in empirical sociological research. However, the widespread practice of comparing the coefficients of a given variable across differently specified models does not warrant the same interpretation in logits and probits as in linear regression. Unlike in linear models, the change in the coefficient of the variable of interest cannot be straightforwardly attributed to the inclusion of confounding variables. The reason for this is that the variance of the underlying latent variable is not identified and will differ between models. We refer to this as the problem of rescaling. We propose a solution that allows researchers to assess the influence of confounding relative to the influence of rescaling, and we develop a test statistic that allows researchers to assess the statistical significance of both confounding and rescaling. We also show why y-standardized coefficients and average partial effects are not suitable for comparing coefficients across models. We present examples of the application of our method using simulated data and data from the National Educational Longitudinal Survey.
Logit Models From Economics And Other Fields
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Author : J. S. Cramer
language : en
Publisher: Cambridge University Press
Release Date : 2003-08-21
Logit Models From Economics And Other Fields written by J. S. Cramer and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-21 with Business & Economics categories.
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.
Linear Probability Logit And Probit Models
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Author : John H. Aldrich
language : en
Publisher: SAGE Publications, Incorporated
Release Date : 1984-11-01
Linear Probability Logit And Probit Models written by John H. Aldrich and has been published by SAGE Publications, Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984-11-01 with Social Science categories.
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.
Polo
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Author : Jacqueline L. Robertson
language : en
Publisher:
Release Date : 1980
Polo written by Jacqueline L. Robertson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980 with Computer programs categories.
Describes the computer program POLO from experimental design, through data input, to program output.
Linear Probability Logit And Probit Models By John H Aldrich And Forrest D Nelson
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Author : John H. Aldrich
language : en
Publisher:
Release Date : 1984
Linear Probability Logit And Probit Models By John H Aldrich And Forrest D Nelson written by John H. Aldrich and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984 with categories.