Regression calculator

Poisson regression calculator

Fit Poisson count regression models with Incidence Rate Ratios (IRR), log coefficients β, deviance goodness-of-fit, and overdispersion checks.

Run this test live in StatLab npm i @statlab/core

When to use it

Use for modeling non-negative integer count outcome data.

Required Inputs

  • Count outcome vector Y (0, 1, 2...)
  • Predictor matrix X
  • Exposure / Offset vector (optional)

Mathematical Formula

log(λ) = β₀ + β₁X₁ + ... + log(Exposure), IRR = exp(β_i)

Reporting Cautions

  • Assumes mean equals variance (E(Y) = Var(Y)); use Negative Binomial regression if data is overdispersed.
  • Include exposure offsets when observation time windows vary.

Code Snippets (Python, R, TypeScript)

Python (SciPy / Statsmodels) 
import statsmodels.api as sm
X = sm.add_constant(x_matrix)
model = sm.GLM(y_vals, X, family=sm.families.Poisson()).fit()
print(model.summary())
R Language 
model <- glm(y ~ x1, data = df, family = "poisson")
summary(model)
TypeScript (@statlab/core) 
import { poissonRegression } from '@statlab/core';
const res = poissonRegression(yVals, xMatrix);

Developer Use Cases & Production Integrations

  • Modeling event counts (e.g. API error rate spikes, bug counts per sprint) in VoxelPulse and VoxelAssurance.
  • Modeling customer click counts per session.

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