Logistic Regression

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Tap a term to see what it means, or .

Calculate

Example: 12 students' hours of study and whether they passed (1) or not (0). How do the odds of passing change with each extra hour, and what is the probability for someone who studies 4 hours? Change the values to work out your own case.

Values of x Outcome y (0 or 1) Predict at x (optional)

Step by step

What each term means

Logit (Log of the Odds): The odds p/(1 − p) compare the probability of y = 1 with that of y = 0; the logarithm stretches that from 0–infinity to the whole line, where a linear model fits.
Intercept (β₀): The logit when x = 0. It sets where the S curve sits.
Coefficient (β₁ x): How much the logit changes per unit of x. e to the β₁ is the odds ratio: the factor that multiplies the odds with each unit.