Logistic Regression Calculator: Free Step-by-Step Tool 2026

Predictor x values (comma or space separated)

Outcome y values (0 or 1)

Logistic Regression Calculator

p = 1 / (1 + e^{−(β₀ + β₁x)})

Intercept β₀—

Coefficient β₁—

Odds ratio e^β1—

Accuracy—

Pseudo R² (McFadden)—

Decision boundary x—

Predict probability at x =

20 points loaded (hours studied vs exam pass). Press Fit logistic model.

This free logistic regression calculator fits a logistic model to your data, returns the coefficients, odds ratio, accuracy and decision boundary, and plots the fitted S-curve — so you can see exactly how the probability of a yes/no outcome changes with your predictor.

How to use the logistic regression calculator

Enter your predictor ($x$) values — any numeric variable such as hours studied, age, or dose.
Enter the outcome ($y$) as 0 or 1 (fail/pass, no/yes), one label per $x$ value.
Press “Fit logistic model” to estimate the coefficients and draw the probability curve.
Type any $x$ in the predict box to get the probability and predicted class for a new case.

What the logistic regression calculator computes

Logistic regression models the probability of the outcome with the sigmoid function:

$$p = \frac{1}{1 + e^{-(\beta_0 + \beta_1 x)}}$$

The calculator estimates $\beta_0$ and $\beta_1$ by maximum likelihood (using gradient descent), then reports:

Output	Meaning
Intercept $\beta_0$	Log-odds of the outcome when $x = 0$.
Coefficient $\beta_1$	Change in log-odds per one-unit increase in $x$.
Odds ratio $e^{\beta_1}$	How the odds multiply for each one-unit rise in $x$.
Accuracy	Share of points classified correctly at the 0.5 threshold.
Pseudo $R^2$	McFadden’s fit measure — higher means a better fit.
Decision boundary	The $x$ where $p = 0.5$ (the model flips its prediction).

📊 Reading the odds ratioIf the odds ratio is 1.5, the odds of the outcome are 50% higher for every one-unit increase in $x$. An odds ratio of 1 means $x$ has no effect; below 1 means $x$ lowers the odds.

Worked example: hours studied vs passing

The built-in example is the classic study-hours dataset: 20 students, their hours of study ($x$), and whether they passed ($y = 1$) or failed ($y = 0$). Fitting it gives roughly $\beta_0 \approx -4.08$ and $\beta_1 \approx 1.50$, so each extra hour multiplies the odds of passing by about $e^{1.5} \approx 4.5$, and the 50% decision boundary lands near 2.7 hours. Study less and you are more likely to fail; study more and the curve climbs toward a near-certain pass.

🤖 Why logistic regression matters in ML

Logistic regression is the simplest classification model and the building block of neural networks — a single neuron with a sigmoid activation. It is supervised learning; see supervised vs unsupervised vs reinforcement learning. To understand how it differs from fitting a straight line, read logistic regression vs linear regression, or fit continuous data with the linear regression calculator.

Assumptions to keep in mind

The outcome is binary (exactly two classes).
The log-odds are linearly related to the predictor.
Observations are independent.
There is enough data in both classes — this tool needs at least one 0 and one 1.

Frequently asked questions

What does a logistic regression calculator do?

It fits a logistic (sigmoid) model to your x and 0/1 data using maximum likelihood, then reports the coefficients, odds ratio, accuracy, pseudo R-squared and decision boundary, and plots the probability curve.

What data do I need?

A numeric predictor x and a binary outcome y coded as 0 or 1, with the same number of values in each box and at least one example of each class.

How is the coefficient interpreted?

The coefficient is the change in log-odds per unit of x. Exponentiating it gives the odds ratio, which is how the odds of the outcome multiply for each one-unit increase in x.

What is the decision boundary?

It is the x value where the predicted probability equals 0.5. Below it the model predicts class 0; above it, class 1.

Is this multiple or simple logistic regression?

This calculator fits simple (one-predictor) logistic regression so you can see the curve. The same maximum-likelihood method extends to multiple predictors.

Key takeaways

A logistic regression calculator turns raw 0/1 data into a fitted probability curve, coefficients and an odds ratio in one click. Use it to model any yes/no outcome, read the decision boundary, and predict the probability for new inputs. Pair it with the logistic vs linear regression guide or the formal reference on Wikipedia.