Logistic regression calculator
Paste predictor X and binary outcome Y coded as 0 or 1. The model contains an intercept and one predictor.
Model fit
| Row | X | Y | Predicted P(Y=1) |
|---|---|---|---|
| Fill in the fields above — the result is computed on this page. | |||
How do I use this page?
- Paste X and binary Y in matching order.
- Check convergence and coefficients.
- Interpret e^β₁ and fitted probabilities.
What this page does not do: The calculator supports one numeric predictor and complete unweighted data. Class separation can prevent a finite estimate; research use also requires diagnostics and confidence intervals.
Further reading
Theory — what is logistic regression?
Simple logistic regression models the log odds of a binary outcome Y=1 as a linear function of a predictor X, with an intercept and one predictor term. The coefficient β₁ shows the change in log odds when X increases by one unit, and e^β₁ is the corresponding odds ratio — an odds ratio of 1.5 means the odds of Y=1 are multiplied by 1.5. Parameters are estimated via maximum likelihood from the pasted X and Y values coded as 0 or 1.
Frequently asked questions
What does an odds ratio of 1.5 mean?
Why does a perfectly separated sample fail?
See also
A/B test calculator
Compare two proportions in an A/B test. Calculate conversion rates, difference, uplift, z score, p value and c
A/B test planning — sample size, MDE and duration
Plan a two-proportion experiment, estimate detectable effect and traffic duration.
ACF and PACF autocorrelation calculator
Calculate ACF and PACF across time-series lags. Review the chart, reference bounds and coefficient table.