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Least likely observations in regression models for categorical outcomes AgEcon
Freese, Jeremy.
This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program least likely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, least likely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given...
Tipo: Journal Article Palavras-chave: Outliers; Predicted probabilities; Categorical dependent variables; Logistic regression; Research Methods/ Statistical Methods.
Ano: 2002 URL: http://purl.umn.edu/116014
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Predicted probabilities for count models AgEcon
Long, J. Scott; Freese, Jeremy.
The post-estimation command prcounts for generating predicted probabilities after using poisson, nbreg, zip, and zinb is introduced and illustrated.
Tipo: Journal Article Palavras-chave: Predicted probabilities; Count models; Research Methods/ Statistical Methods.
Ano: 2001 URL: http://purl.umn.edu/115935
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Review of Statistics with Stata (Updated for Version 7) AgEcon
Freese, Jeremy.
This article reviews Statistics with Stata (Updated for Version 7) by Hamilton.
Tipo: Journal Article Palavras-chave: Introduction to Stata; Research Methods/ Statistical Methods.
Ano: 2002 URL: http://purl.umn.edu/115964
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