Survival & Reliability calculator

Weibull reliability and failure rate calculator

Estimate Weibull shape parameter β (slope), scale parameter η (characteristic life), Mean Time To Failure (MTTF), and hazard rate functions.

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

When to use it

Use for lifetime data modeling and reliability engineering.

Required Inputs

  • Failure / Lifetime data points
  • Censoring indicator array
  • Estimation method (MLE / Least Squares Rank)

Mathematical Formula

R(t) = exp[ -(t/η)^β ], h(t) = (β/η)(t/η)^(β-1)

Reporting Cautions

  • β < 1 indicates decreasing failure rate; β = 1 constant exponential rate; β > 1 wear-out rate.
  • Minimum sample size N ≥ 10 recommended for stable MLE shape estimation.

Code Snippets (Python, R, TypeScript)

Python (SciPy / Statsmodels) 
from scipy import stats
shape, loc, scale = stats.weibull_min.fit(durations, floc=0)
print(f"beta={shape:.2f}, eta={scale:.2f}")
R Language 
library(flexsurv)
flexsurvreg(Surv(time, status) ~ 1, dist = "weibull")
TypeScript (@statlab/core) 
import { weibullFit } from '@statlab/core';
const fit = weibullFit(durations);

Developer Use Cases & Production Integrations

  • Modeling hardware failure modes (infant mortality β<1 vs wear-out β>1) for cloud infrastructure.
  • Predicting component reliability thresholds in VoxelAssurance quality audits.

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