Logistic Regression

Explanatory binary logistic regression with odds ratios (and a Firth option).

Overview

Fits an explanatory binary logistic regression (statsmodels): predict membership in a group / binary outcome from numeric predictors, reporting coefficients, standard errors, z, p-values, odds ratios with confidence intervals and the direction of each effect. It can build binary predictors from text/keyword columns, fit several targets at once (one tab each plus a Summary pivot), and use Firth penalisation to handle separation.

Inputs

Data (Table) — data with a binary target and numeric predictors.

Outputs

Coefficients (Table) — model coefficients and statistics.
Predictions (Table) — data with predicted probability/class.

Controls

Group / target + Positive class — the binary outcome and which class counts as 1.
Additional targets — pick extra binary targets (each gets its own results tab + a Summary tab).
Predictors (numeric) — tick the numeric predictors (All / None).
Build predictors from text/keywords — turn the top terms of a text field into binary predictors: Column (Title/Abstract/Keywords/combination/References), Top N terms, Min occurrences, Remove stopwords.
Include intercept, Standardize predictors (z-score).
Firth penalized — penalised MLE that fixes quasi/complete separation.
Summary cells — what the multi-target Summary pivot shows (coefficient / p-value / both).

Actions: Build binary predictors, Fit model.