= Solution
Use \b[<logistic regression>] when the <response variable> is binary, for example whether a lecturer missed a deadline. It relates the event's <log odds> to one or more predictors, producing fitted <probabilities> between zero and one. Exponentiating a coefficient gives an <odds ratio> for a specified predictor change, conditional on the other model terms. An <odds ratio> is not generally a ratio of probabilities; the corresponding probability change depends on the starting probability and other predictors.
Report the outcome coding, estimated associations with <confidence intervals>, and relevant model checks. Sparse events, <complete separation> and dependent observations can make ordinary estimates or inferential procedures unreliable; repeated observations may need a <generalized linear mixed model>. For prediction, evaluate calibration and discrimination on data not used to tune the model. An adjusted association in an <observational study> does not automatically identify a causal effect.
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