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SigmaCalc
Plug in fitted coefficients and an input value to see the predicted probability the sigmoid function produces.
Fitting a logistic regression model (finding the best b₀ and b₁ from training data) requires iterative optimization beyond what a single-formula calculator can do — this tool evaluates a model you already have, converting coefficients and an input into a predicted probability.
A plain linear model can predict nonsensical probabilities like 1.4 or −0.2. The sigmoid function used here squashes any input into the valid 0-to-1 range, which is exactly why logistic regression is the standard tool for binary outcomes.
Predicted probability
0.599
p = 1 ÷ (1 + e^−(b₀ + b₁x))
What you entered
Linear combination (log-odds)
-4 + 0.08 × 55= 0.4Apply the logistic (sigmoid) function
1 ÷ (1 + e^(−0.4))= 0.5987Result
Predicted probability: 0.599
With these coefficients, an input of 55 maps to a predicted probability of 59.87% — logistic regression squashes any linear combination into a valid 0–1 probability.