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Conditional on the covariates, the binary responses are independent with Bernoulli distributions:Thus $p_i=(1+e^{-{\eta }_i}{)}^{-1}$, the logistic regression model. The maximum-likelihood estimates areUsing the reported age standard error, an approximate 95% Wald confidence interval isThis interval concerns the age coefficient in log odds per year, with income and loan amount held fixed.

Two packs contain $40$ cigarettes. Holding height fixed, increasing average consumption from zero to two packs per day changes the log odds byThe corresponding odds ratio is thereforeThus the model estimates that a two-pack-per-day smoker has about $2.52$ times the odds of heart disease of an otherwise comparable nonsmoker. This is an association in the fitted observational model, rather than by itself a causal conclusion.

Specify distributions of measurements separately by observation pattern and vary assumptions about unseen values that the observed data do not identify. For example, shift the imputed binary-response log odds for nonresponders away from its missing at random prediction and refit the substantive analysis. This investigates missing not at random departures rather than testing them from observed data.