Estimate percentiles and probabilities for Regression with Life Data

Stat > Reliability/Survival > Regression with Life Data > Estimate

Percentile and Probability Estimation

Enter new predictor values: Enter new values or columns of new values. The number of new predictor values must equal the number of predictors in the model. The first value or column corresponds to the first variable in the model, the second value or column corresponds to the second variable, and so on. For example, you might enter the design value, or common field condition, for the units.
Use predictor values in data (storage only): Use the predictor values from the data to estimate percentiles and/or survival probabilities. To obtain results, you must specify to store at least one of the following: percentiles, confidence limits, standard error, or probabilities.

Estimate percentiles for percents

Enter the percents for which you want to estimate percentiles. Percents for percentiles 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 units that will fail. The n^th percentile has n% of the observations below it, and (100–n)% of observations above it.

By default, Minitab estimates the 50^th percentile. If you want to look at the beginning, middle, and end of the product's lifetime for a given predictor value, enter 10 50 90 (the 10^th, 50^th, 90^th percentiles). Minitab then estimates how long it takes for 10% of the units to fail, 50% of the units to fail, and 90% of the units to fail.

Store percentiles

Indicate whether you want to store the percentiles, the standard error of the percentiles, or the confidence limits for the percentiles. Minitab stores the values in separate columns of the worksheet.

Estimate probabilities for times

Enter the times for which you want to estimate survival probabilities or cumulative failure probabilities.

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.

Store probabilities

Indicate whether you want to store the probabilities (for survival or for cumulative failure) or the confidence limits for the probabilities. Minitab stores the values in separate columns of 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.