Specify how to estimate the nonparametric probabilities, the type of probabilities to
estimate, and details about confidence intervals.
- Estimation method for
right-censored or complete data
-
- Kaplan-Meier
- Estimate the parameters using the Kaplan-Meier method. Minitab
displays a hazard function plot and a survival function plot
based on Kaplan-Meier estimates.
- Actuarial with equally spaced time intervals
- Estimate the parameters using the actuarial method. Minitab
displays a hazard function plot and survival function plot based
on actuarial estimates.
- Select this option to use equally spaced time intervals. Enter
the beginning time, the ending time, and the step size for the
intervals. For example, if you enter 0 to 100 in steps of 20,
Minitab plots the results using the time intervals 0 to 20, 20
to 40, and so on, up to 80 to 100.
- Actuarial with unequally spaced time intervals
- Estimate the parameters using the actuarial method. Minitab
displays a hazard function plot and survival function plot based
on actuarial estimates.
- Select this option to use unequally spaced time intervals. Enter
a series of numbers or a column of numbers. Omit the starting
time of the first interval because the first interval always
starts at 0. For example, if you enter 4 6 8 10 20 30, Minitab
plots the results using the time intervals 0 to 4, 4 to 6, 6 to
8, 8 to 10, 10 to 20, and 20 to 30.
- The following column illustrates the same intervals in the
worksheet.
| C1 |
| Intervals |
| 4 |
| 6 |
| 8 |
| 10 |
| 20 |
| 30 |
- Estimation method
for interval-censored data or warranty data
-
- Turnbull
- Estimate the parameters using the Turnbull method. Minitab
displays a survival function plot based on Turnbull
estimates.
- Actuarial
- Estimate the parameters using the actuarial method. Minitab
displays a hazard function plot and a survival function plot
based on actuarial estimates. The time intervals are the same as
the intervals in the data.
- Estimate
- Select the probabilities to show in the results.
- Survival probabilities
- 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
- Estimate the likelihood that units fail before a given time. The
cumulative failure probability is 1 minus the survival
probability.
- 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 interval
-
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.
