Estimate event probability, odds ratios, and classification risk. Inspect coefficient impact with resampling-based interval summaries. Make binary outcome analysis clearer for practical, data-driven decisions.
Use the responsive grid below. Large screens show three columns, medium screens show two, and small screens show one.
Linear predictor: z = β₀ + Σ(βᵢ × xᵢ)
Predicted probability: p = 1 / (1 + e-z)
Predicted odds: odds = ez
Odds ratio for each predictor: ORᵢ = eβᵢ
Coefficient interval: βᵢ ± zα/2 × SEboot
OR interval: exponentiate the coefficient interval limits.
Bootstrap probability interval: simulate repeated coefficient draws, convert each draw into a probability, then read percentile limits.
Classification rule: predict class 1 when p ≥ threshold; otherwise predict class 0.
| Outcome | Age | BMI | Smoker | Exercise Hours | Family History | Cholesterol Ratio |
|---|---|---|---|---|---|---|
| 1 | 52 | 31.0 | 1 | 3 | 1 | 4.8 |
| 0 | 44 | 26.2 | 0 | 5 | 0 | 3.4 |
| 1 | 59 | 33.8 | 1 | 1 | 1 | 5.2 |
| 0 | 38 | 24.9 | 0 | 6 | 0 | 3.1 |
| 1 | 61 | 30.5 | 1 | 2 | 1 | 4.9 |
| 0 | 47 | 27.1 | 0 | 4 | 0 | 3.6 |
It estimates the linear predictor, event probability, odds, predictor odds ratios, interval limits, and resampling-based stability for a binary logistic regression scenario.
Bootstrap standard errors reflect uncertainty from repeated resampling. They help you judge whether each coefficient and predicted probability remain stable across changing samples.
The predicted class is the outcome assigned after comparing the estimated probability with your chosen threshold. Above the threshold predicts 1; below predicts 0.
An odds ratio above one raises event odds as the predictor increases by one unit. An odds ratio below one lowers event odds.
Classification stability shows how often the model keeps the same class during repeated resampling draws. Higher values suggest more reliable threshold-based decisions.
The base event rate provides a null reference for comparing your fitted prediction against a simpler benchmark. It helps compute pseudo R² and related diagnostics.
Yes. Set unused predictor coefficients, values, and bootstrap standard errors to zero, or replace their labels with placeholders to keep the layout consistent.
No. It is best for quick scenario analysis, interpretation, and reporting. Use dedicated statistical software when you need full estimation, diagnostics, and validation.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.