Minitab offers many different reliability/survival analyses.

Minitab's reliability test plans have two main functions. One is to determine the sample size and testing time needed to estimate model parameters. The other is to demonstrate that you have met specified reliability requirements.

A test plan includes:

- The number of units you need to test.
- A stopping rule: the amount of time you must test each unit or the number of failures that must occur.
- Success criterion: the number of failures allowed while the test still passes (for example, all units are tested for the specified amount of time and there are no failures).

- Demonstration test plan
- To determine the test time or sample size required to demonstrate reliability requirements, create a demonstration test plan. In Minitab, choose .
- Estimation test plan
- To determine the sample size required to estimate reliability parameters, create an estimation test plan. In Minitab, choose .
- Accelerated life test plan
- To determine sample sizes for an accelerated life test, create an accelerated life test plan. In Minitab, choose .

Use Minitab's reliability distribution analysis commands to understand the lifetime characteristics that are under study.

- Distribution ID plot
- Draw probability plots from your choice of 11 common reliability distributions to examine distribution fit.
- To create a distribution ID plot with right-censored data, in Minitab, choose .
- To create a distribution ID plot with arbitrarily-censored data, in Minitab, choose .

- Distribution overview plot
- Assess the fit of the chosen distribution and view summary graphs of your data.
- To create a distribution overview plot with right-censored data, in Minitab, choose .
- To create a distribution overview plot with arbitrarily-censored data, in Minitab, choose .

- Parametric distribution analysis
- Estimate percentiles, survival probabilities, and cumulative failure probabilities using a chosen reliability distribution.
- To perform a parametric distribution analysis with right-censored data, in Minitab, choose .
- To perform a parametric distribution analysis with arbitrarily-censored data, in Minitab, choose .

- Nonparametric distribution analysis
- Estimate percentiles, survival probabilities, and cumulative failure probabilities using a nonparametric method.
- To perform a nonparametric distribution analysis with right-censored data, in Minitab, choose .
- To perform a nonparametric distribution analysis with arbitrarily-censored data, in Minitab, choose .

Use Minitab's warranty analysis commands to analyze historical warranty data and predict the number and cost of warranty claims in the future. Warranty analysis helps you allocate resources to address future product failures adequately.

- Pre-process warranty data
- Convert warranty data to interval-censored data. In Minitab, choose .
- Warranty prediction
- Predict number or cost of failures due to warranty claims. In Minitab, choose .

Use Minitab's growth curves to analyze life data from a repairable system. A repairable system is one in which the parts are repaired instead of being replaced when they fail. Growth curves can show whether a trend exists in times between successive failures of a repairable system and whether system failures are becoming more frequent, less frequent, or remaining constant.

- Parametric growth curve
- Estimate growth curves of the mean number of repairs and the rate of occurrence of failure (ROCOF) over time using a power-law process or a homogeneous Poisson process. In Minitab, choose .
- Nonparametric growth curve
- Estimate growth curves of the mean cost of maintaining the system or the mean number of repairs over time without making assumptions about the distribution of the cost or number of repairs.In Minitab, choose .

Use Minitab's regression with life data commands to investigate the relationship between failure time and one or more predictors.

- Accelerated life testing
- Model product performance (usually failure times) at extreme stress levels so that you can extrapolate the results back to normal conditions. Models can include two predictors, and are usually used to assess highly reliable products. In Minitab, choose .
- Regression with life data
- Perform a regression to determine how different factors and covariates affect the lifetime of your product. In Minitab, choose .

Use probit analysis when you apply a stress or stimulus to a number of units and record whether each unit failed or survived. A probit analysis is used to evaluate a binary response variable. The analysis estimates percentiles, survival probabilities, cumulative failure probabilities and draws probability plots. In Minitab, choose

.