Interpreting probability models
Subtitle: logit, probit, and other generalized linear models
By: Tim Futing Liao.
Series: Sage university papers series.; no. 07-101
Publisher: Sage
Published: 1994
Place of publication: Thousand Oaks, Calif
Language: eng
Number of pages: 88
Subjects: Linear models (Statistics); Logits.; Probits.
Description: "What is the probability that something will occur, and how is that probability altered by a change in some independent variable? Aimed at answering these questions, Liao introduces a systematic way for interpreting a variety of probability models commonly used by social scientists. Since much of what social scientists study are measured in noncontinuous ways and thus cannot be analyzed using a classical regression model, it is necessary for scientists to model the likelihood (or probability) that an event will occur. This book explores these models by reviewing each probability model and by presenting a systematic way for interpreting results. Beginning with a review of the generalized linear model, the book covers binary logit and probit models, sequential logit and probit models, ordinal logit and probit models, multinomial logit models, conditional logit models, and Poisson regression models."--Pub. desc.
Notes: Includes bibliographical references (p. 85.87).