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Interpreting probability models logit, probit, and other generalized linear models

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  • ""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."

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  • "Einführung"
  • "Electronic books"@en

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  • "Interpreting probability models logit, probit, and other generalized linear models"@en
  • "Interpreting probabilizy models : logit, probit, and other generalized linear models"
  • "Interpreting probability models : logit, probit and other generalized linear models"@en
  • "Interpreting probability models : logit, probit and other generalized linear models"
  • "Interpreting probability models : logit, probit and other generalized l inear models"
  • "Interpreting probability models logit, probit and other generalized linear models"
  • "Interpreting probability models : logit, probit, and other generalized models"@en
  • "Interpreting probability models : logit, probit, and others generalized linear models"
  • "Interpreting probability models : logit, probit, and other generalized linear models"

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