Number of obs – This is the number of observations that were used in the analysis. The value -80.11818 has no meaning in and of itself rather, this number can be used to help compare nested models.Ĭ. Log likelihood – This is the log likelihood of the final model. Model Summary Logit estimates Number of obs c = 200 When the difference between successive iterations is very small, the model is said to have “converged”, the iterating is stopped and the results are displayed. At each iteration, the log likelihood increases because the goal is to maximize the log likelihood. At the next iteration, the predictor(s) are included in the model. (Remember that logistic regression uses maximum likelihood, which is an iterative procedure.) The first iteration (called iteration 0) is the log likelihood of the “null” or “empty” model that is, a model with no predictors. This is a listing of the log likelihoods at each iteration. Iteration Log Iteration 0: log likelihood = -115.64441 Logit honcomp female read science Iteration 0: log likelihood = -115.64441 We do not advocate making dichotomous variables out of continuous variables rather, we do this here only for purposes of this illustration. The variable female is a dichotomous variable coded 1 if the student was female and 0 if male.īecause we do not have a suitable dichotomous variable to use as our dependent variable, we will create one (which we will call honcomp, for honors composition) based on the continuous variable write. These data were collected on 200 high schools students and are scores on various tests, including science, math, reading and social studies ( socst).
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How to Interpret Logistic Regression output in Stata How to Interpret Logistic Regression output in Stata This page shows an example of logistic regression regression analysis with footnotes explaining the output.
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