Use Parametric Distribution Analysis (Arbitrary Censoring) to estimate the overall reliability of your system when your data follow a parametric distribution and are arbitrarily censored. Arbitrarily-censored data include left-censored observations and/or interval-censored observations. For more information, go to Data censoring.

You can select from 11 distributions to model your lifetime data: smallest extreme value, Weibull, 3-parameter Weibull, exponential, 2-parameter exponential, normal, lognormal, 3-parameter lognormal, logistic, loglogistic, and 3-parameter loglogistic. For more information on fitting a distribution to your data, go to Distribution fit for reliability analysis.

Based on the fitted distribution that you select, you can do the following:

- Display parameter estimates and distribution characteristics, such as mean time to failure (MTTF)
- Estimate percentiles, survival probabilities, cumulative failure probabilities, and their confidence intervals
- Display survival plots, cumulative failure plots, and hazard plots to graphically analyze the probability of failure or survival
- Assess the fit of the selected distribution with a probability plot

When your product fails in different ways, you can enter failure mode information for this analysis to evaluate the impact of each type of failure on the overall reliability. Each failure mode is assumed to be independent and may be modeled by different distributions. By analyzing each failure mode individually, you can more easily prioritize your improvement efforts.

To perform a parametric distribution analysis for arbitrarily-censored data, choose

.- If your data contain only exact failure times and/or right-censored observations, use Parametric Distribution Analysis (Right Censoring).
- To determine which parametric distribution best fits your data, use Distribution ID Plot (Arbitrary Censoring).
- If no parametric distribution fits your data adequately, use Nonparametric Distribution Analysis (Arbitrary Censoring).