Specify percentiles and probabilities to estimate for Parametric Distribution Analysis

Stat > Reliability/Survival > Distribution Analysis > Parametric Distribution Analysis > Percentiles/Probabilities

Estimate percentiles for these additional percents

Enter the percents for which you want to estimate percentiles. A percent for a percentile is the percentage of items that are expected to fail by a particular time (percentile). Therefore, each value that you enter must be between 0 and 100 and should indicate the percentage of items that will fail. The nth percentile has n% of the observations below it, and (100 – n)% of observations above it.

Estimate

Select whether to estimate additional survival probabilities or cumulative failure probabilities. Then, enter times for the probabilities.

Survival probabilities for the following times: Estimate the proportion of units that survive beyond a given time. Use these values to determine whether your product meets reliability requirements or to compare the reliability of two or more designs of a product. For more information, go to What is the survival probability?.
Cumulative failure probabilities for the following times: Estimate the likelihood that units fail before a given time. The cumulative failure probability is 1 minus the survival probability.
Time values: Enter one or more times separated by spaces or enter a single column from the worksheet.

Confidence level

Enter a confidence level between 0 and 100. Usually a confidence level of 95% works well. A 95% confidence level indicates that you can be 95% confident that the interval contains the true population parameter. That is, if you collected 100 random samples from the population, you could expect approximately 95 of the samples to produce intervals that contain the actual value for the population parameter (if all the data could be collected and analyzed).

A lower confidence level, such as 90%, produces a narrower confidence interval and may reduce the sample size or testing time that is required. However, the likelihood that the confidence interval contains the population parameter decreases.

A higher confidence level, such as 99%, increases the likelihood that the confidence interval contains the population parameter. However, the test may require a larger sample size or a longer testing time to obtain a confidence interval that is narrow enough to be useful.

Confidence intervals

From the drop-down list, indicate whether you want Minitab to display a two-sided confidence interval (Two-sided) or a one-sided confidence interval (Lower bound or Upper bound). A one-sided interval generally requires fewer observations and less testing time to be statistically confident about the conclusion. Many reliability standards are defined in terms of the worst-case scenario, which is represented by a lower bound.

Note

You can specify a confidence level and calculate confidence intervals only when you select Maximum Likelihood from Estimation Method in Distribution Fitting.