Multivariable logistic analysis
Purpose
Model associations with the odds of a binary outcome.
Data and assumptions
The outcome must have exactly two observed categories. Set the event code explicitly. Encode categorical predictors jointly and select their reference levels. Ordinary maximum likelihood is unreliable under complete separation.
Configure this method
Enter X and covariates jointly using common complete cases. Choose an adjustment set from the research question rather than mechanically screening only by univariable p-values.
Variables
| Control | Input |
|---|---|
| Outcome Y | Select one variable |
| Predictors X | Select variables |
| Control Variables | Select variables |
| Treat as categorical | Select variables |
| Reference categories | Choose categorical reference levels |
Analysis settings
| Parameter | Choices | Default |
|---|---|---|
| Outcome event code | Set as needed | 1 |
Analysis parameters remain visible in the main configuration area. Advanced formatting contains only the table title and numeric decimals. Methods that calculate intervals also show the significance level; 0.05 corresponds to 95% intervals.
Read the results
Report odds ratios, confidence intervals and p-values. An odds ratio is not a probability ratio or risk ratio. For multi-level predictors also review the joint test in raw output.
Workflow and exports
- Upload the dataset and choose the method from the sidebar or search.
- Select the variables above, set the design-specific options, then run the analysis.
- Inspect the paper-style result. Reconfigure from the result area, or open raw output for diagnostics and sample details.
- Rename and keep result tabs as needed. Export Word, Excel, CSV or TXT; batch export can include the selected AI interpretation. Word retains figures. AI text should be checked against the estimates.