Logistic regression
Regression · Method and input reference
Rows are independent observations. No missing values. Truncated and Tobit models use a lower bound and Gaussian errors. Exponential fits log(y) by OLS. Logistic requires a binary 0/1 outcome. Synthetic example: 240 adults: readmission (0/1), age and BMI. Events overlap across predictor values; no perfect separation.
Open calculator · Compare methods in the decision matrix
When to consider it
- Dichotomous outcome predicted by one or more variables; returns odds ratios.
Assumptions and scope
- Observations must be independent between subjects; repeated rows from one subject are not independent samples.
- Binary outcome 0/1; inspect separation, event counts and functional form of continuous predictors.
Required inputs
- Data: outcome first, then predictor columns — Numeric observations; preserve the row and column structure described above.
Review missing values and data coding before running. Sample inputs are synthetic teaching examples.
Implementation
Lifescio validates inputs and formats results; listed functions are the principal calculation routines, not an exhaustive transitive dependency list.
- statsmodels 0.14.4: statsmodels.api.Logit
These are pinned library versions; saved results record the versions actually used at runtime.