Estimate percentiles and probabilities for Probit Analysis

Stat > Reliability/Survival > Probit Analysis > Estimate

Estimate percentiles for these additional percents

To estimate percentiles in addition to the percentiles that Minitab estimates by default, enter one or more percents (or a column of percents) for the percentile estimates. For example, to estimate the stress level at which 99.9% of units fail, a researcher enters a percent value of 99.9.

Estimate probabilities for these stress values

To estimate probabilities for specific stress values, enter one or more stress values (or a column of stress values). For example, to estimate the percentage of items that will fail at a temperature of 220, a researcher enters 220.

Estimate survival probabilities: Estimate the proportion of units that survive beyond the stress value that you entered. For more information, go to What is the survival probability?
Estimate cumulative failure probabilities: Estimate the likelihood that units fail before the stress value that you entered. The cumulative failure probability is 1 minus the survival probability.

Confidence Intervals

Fiducial: Display a confidence interval based on fiducial statistical theory. This confidence interval considers the unknown population parameters to be random variables. For a 100(x)% fiducial confidence interval, the probability that the population parameter falls within the interval is (x).
Normal approximation: Display a standard confidence interval based on normal approximation. This confidence interval considers the unknown population parameters to be fixed values and the confidence interval itself to be random. For a 100(x)% standard confidence interval, the probability that a random sample will produce an interval that contains the fixed value of the population parameter is (x).

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.