Generalised linear models: logistic and probit regression for a binary outcome (yes/no, success/failure, event/no-event), and Poisson regression for a count outcome (number of events, visits, occurrences).

Examples

Logistic, single and two-group

• Simple logistic regression: one continuous predictor relapse ~ biomarker_level — one continuous predictor on a yes/no outcome, no covariates.
• Logistic two-group comparison (binary predictor) remission ~ treatment — compare event rates between two groups (chi-square recast).

Logistic, adjusted and categorical

• Logistic regression with a categorical predictor survived ~ habitat — a multi-level categorical predictor on a binary outcome.
• Multiple logistic regression: covariate-adjusted employed ~ years_education + age + gender — a focal predictor on a binary outcome, covariate-adjusted.
• Logistic regression: continuous predictor plus categorical control employed ~ experience_years + region — a continuous predictor plus a categorical control (parallel slopes).

Logistic interactions

• Logistic continuous-by-continuous moderation relapse ~ biomarker_level * age — do two continuous predictors interact on a binary outcome?
• Logistic treatment-by-moderator interaction (binary x continuous) remission ~ treatment * biomarker_level — a binary-by-continuous interaction on a yes/no outcome.
• Logistic 2x2 factor-by-factor interaction voted ~ gender * urban — two binary factors interacting on a binary outcome.
• Logistic three-way interaction germinated ~ light * moisture * temperature — a three-way interaction on a binary outcome.

Count and probit outcomes

• Poisson count regression: clinic visits predicted by treatment and age clinic_visits ~ treatment + age — a count outcome, log-link Poisson regression with a binary and a continuous predictor.
• Probit regression: household income predicting voter turnout, controlling for region voted ~ income + region — a binary outcome via the probit link, one continuous predictor plus a categorical control.