Multiple linear 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: 180 adults: blood pressure, age, BMI and treatment (0/1). Covariates vary independently and the response includes noise.
Open calculator · Compare methods in the decision matrix
When to consider it
- Continuous outcome predicted by two or more predictors; controls for covariates.
Assumptions and scope
- Observations must be independent between subjects; repeated rows from one subject are not independent samples.
- Use the outcome and predictor coding required by the calculator; review missing data and model diagnostics.
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.OLS
These are pinned library versions; saved results record the versions actually used at runtime.