2006Unpublished venueRequires access

Predicting Dichotomous Outcomes: Logistic Regression, and Receiver Operating Characteristic

Joseph D. Schulman

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Abstract

The prelims comprise: The aim of logistic regression The logistic model Using Stata for logistic regression analysis The receiver operating characteristic curve Indicator variables in logistic regression Testing the linear trend Evaluating how well the logistic model fits the data Using Stata to compute sensitivity and specificity Key points in evaluating a logistic model

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What this paper is about

The prelims comprise: The aim of logistic regression The logistic model Using Stata for logistic regression analysis The receiver operating characteristic curve Indicator variables in logistic regression Testing the linear trend Evaluating how well the logistic model fits the data Using Stata to compute sensitivity and specificity Key points in evaluating a logistic model

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Available abstract

The prelims comprise: The aim of logistic regression The logistic model Using Stata for logistic regression analysis The receiver operating characteristic curve Indicator variables in logistic regression Testing the linear trend Evaluating how well the logistic model fits the data Using Stata to compute sensitivity and specificity Key points in evaluating a logistic model

Key concepts: Logistic regression, Logistic model tree, Statistics, Receiver operating characteristic, Multinomial logistic regression, Regression analysis, Logistic distribution, Regression diagnostic

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