Specify the estimation methods for Nonparametric Distribution Analysis (Right Censoring)

Stat > Reliability/Survival > Distribution Analysis (Right Censoring) > Nonparametric Distribution Analysis > Estimate

Estimation Method

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: Estimate the parameters using the actuarial method. Minitab displays a hazard function plot and survival function plot based on actuarial estimates.
Specify time intervals as:
- 0 to _ by _: Use equally spaced time intervals. Enter numbers to indicate the time intervals on the plots. For example, if you enter 0 to 100 by 20, Minitab plots the results using the time intervals 0-20, 20-40, and so on, up to 80-100.
- Enter endpoints of intervals: Select to use unequally spaced time intervals and enter a series of numbers, or a column of numbers. For example, if you enter 0 4 6 8 10 20 30, Minitab plots the results using the time intervals 0-4, 4-6, 6-8, 8-10, 10-20, and 20-30.

Estimate 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?
Estimate 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 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.