Last edited by Zulkikinos

Thursday, July 23, 2020 | History

4 edition of **Regression models for categorical dependent variables using Stata** found in the catalog.

Regression models for categorical dependent variables using Stata

J. Scott Long

- 310 Want to read
- 19 Currently reading

Published
**2003**
by Stata Corporation in College Station, TX
.

Written in English

**Edition Notes**

Statement | J. Scott Long, Jeremy Freese. |

Contributions | Freese, Jeremy. |

ID Numbers | |
---|---|

Open Library | OL16197034M |

ISBN 10 | 1881228827 |

Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression models for categorical data. The third edition is a complete rewrite of the book. Factor variables and the margins command changed how the effects of variables can be estimated and interpreted. In addition, the authors’ views on interpretation have . Downloadable! Author(s): J. Scott Long & Jeremy Freese. Abstract: Regression Models for Categorical Dependent Variables Using Stata, 2nd Edition, by J. Scott Long and Jeremy Freese, shows how to fit and interpret regression models for categorical data with Stata. Nearly 50% longer than the previous edition, the book covers new topics for fitting and interpretating models included in.

References Here are some places to read more about regression models with count data. Agresti, A. () Categorical Data Analysis (2nd ed). New York: Wiley. Agresti, A. () An Introduction to Categorical Data Analysis. New York: Wiley. Long, S. J. () Regression Models for Categorical and Limited Dependent Variables. The results presented in the book can be replicated in Stata with the SPost commands described here. The data in Stata format are here. The data in Excel format are here. J. Scott Long, (translated ), Regression Models for Categorical and Limited Dependent Variables. Taipei, .

Regression Models for Categorical Dependent Variables Using Stata, Third Edition: Long, J Scott, Freese, Jeremy: : Libros/5(13). Long, J. S., & Freese, J. (). Regression Models for Categorical Dependent Variables Using Stata (2nd ed.). College Station, TX: Stata Press. has been cited by the following article: TITLE: Teachers’ Learning Activities in the Workplace: How Does Teacher Education Matter? AUTHORS: Joakim Caspersen.

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Regression Models for Categorical Dependent Variables Using Stata, Third Edition, by J. Scott Long and Jeremy Freese, is an essential reference for those who use Stata to fit and interpret regression models for categorical data.

Although regression models for categorical dependent variables are common, few texts explain how to interpret such models; this text decisively fills the void.

Regression Models for Categorical Dependent Variables Using Stata, Third Edition $ Only 17 left in stock (more on the way).Cited by: Regression Models for Categorical Dependent Variables Using Stata, Second Edition, fills this void, showing how to fit and interpret regression models for categorical data with Stata.

The authors also provide a suite of commands for hypothesis testing Although regression models for categorical dependent variables are common, few texts explain how to interpret such models/5.

Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression models for categorical data.

The third edition is a complete rewrite of the book. Factor variables and the margins command Regression models for categorical dependent variables using Stata book how the effects of variables can be estimated and interpreted. In addition, the authors' views on interpretation have evolved.

Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression models for categorical data.

The third edition is a complete rewrite of the book. Factor variables and the margins command changed how the effects of variables can be estimated and interpreted.

TY - BOOK. T1 - Regression models for categorical dependent variables using stata. AU - Long, J. Scott. AU - Freese, Jeremy. PY - Y1 - M3 - Book. SN - BT - Regression models for categorical dependent variables using stata.

PB - Stata Press. CY - Cited by: Translated by LightStone, Stata's distributor in Japan, Regression Models for Categorical Dependent Variables Using Stata, Third Edition, by J.

Scott Long and Jeremy Freese, is an essential reference for those who use Stata to fit and interpret regression models for categorical data.

Regression Models for Categorical Dependent Variables Using Stata Second Edition J. SCOTT LONG Department of Sociology Indiana University Bloomington, Indiana JEREMY FREESE Department of Sociology University of Wisconsin-Madison Madison, Wisconsin A Stata Press Publication StataCorp LP College Station, Texas.

The speciﬁc cross-sectional models that we consider, along with the corresponding Stata commands, are. Binary outcomes: binary logit (logit) and binary probit (probit). Ordinal outcomes: ordered logit (ologit) and ordered probit (oprobit). Nominal outcomes: multinomial logit (mlogit) and conditional logit (clogit).

"Regression Models for Categorical and Limited Dependent Variables excels at explaining applications of nonlinear regression models The book provides much practical guidance for the estimation, identification, and validation of models for CLDVs.

Each chapter is interspersed with exercises and helpful by: Regression Models for Categorical Dependent Variables Using Stata, 2nd Edition, by J.

Scott Long and Jeremy Freese, shows how to fit and interpret regression models for categorical data with Stata. Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression models for categorical data.

The third edition is a complete rewrite of the book. Factor variables and the margins command changed how the effects of variables can be estimated and interpreted/5(13). When the dependent variable is a categorical variable, the three models (referred to as probability models) that can be used are the linear probability model, the logit regression model, and the probit regression model.

In this chapter, we describe all of these models for handling categorical variables and provide several applications in finance. mathematical Regression Models for Categorical and Limited Dependent Variables is one candidate for a companion text.

However, if researchers could read only one. Regression Models for Categorical Dependent Variables Using Stata, Third Edition began shipping on September 2, This edition is a complete rewrite of the second edition, taking full advantage of Stata's margins command and factor variable notation.

Many new methods of interpretation are introduced using SPost13, a complete rewrite of. Regression with Categorical Dependent Variables Montserrat Guillén This page presents regression models where the dependent variable is categorical, whereas covariates can either be categorical or continuous, using data from the book Predictive Modeling Applications in Actuarial : Entorns Web Ub.

Although regression models for categorical dependent variables are common, few texts explain how to interpret such models. Regression Models for Categorical Dependent Variables Using Stata, Second Edition, fills this void, showing how to fit and interpret regression models for categorical data with Stata.

The authors also provide a suite of commands for hypothesis testing and model 4/5(3). "Regression Models for Categorical Dependent Variables Using Stata, Second Edition", fills this void, showing how to fit and interpret regression models for categorical data with Stata.

The authors also provide a suite of commands for hypothesis /5(10). Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression models for categorical data.

The third edition is a complete rewrite of the book. Factor variables and the margins command changed how the effects of variables can be estimated and interpreted. In addition, the authors' views on interpretation have /5(13). "The goal of Regression Models for Categorical Dependent Variables Using Stata, Third Edition is to make it easier to carry out the computations necessary to fully interpret regression models for categorical outcomes by using Stata's margins command.

Because the. Stanford Libraries' official online search tool for books, media, journals, databases, government documents and more.

Regression Models for Categorical Dependent Variables Using Stata in SearchWorks catalog.Get this from a library! Regression models for categorical dependent variables using Stata. [J Scott Long; Jeremy Freese] -- After reviewing the linear regression model and introducing maximum likelihood estimation, Long extends the binary logit and probit models, presents multinomial and conditioned logit models .Regression Models for Categorical Dependent Variables using Stata is an essential book for Stata users interested in categorical data analysis.

3 References Long, J. S. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, CA: .