Distribution Analysis Overview
see also 

Use Minitab's distribution analysis commands to understand the lifetime characteristics of a product, part, person, or organism. For instance, you might want to estimate how long a part is likely to last under different conditions, or how long a patient will survive after a certain type of surgery.

Your goal is to estimate the failure-time distribution of a product. You do this by estimating percentiles, survival probabilities, cumulative failure probabilities, and distribution parameters and by drawing survival plots, cumulative failure plots, or hazard plots. You can use either parametric or nonparametric estimates. Parametric estimates are based on an assumed parametric distribution, while nonparametric estimates assume no parametric distribution.

Choosing a distribution analysis command

How do you know which distribution analysis command to use? You need to consider two things: 1) the type of censoring you have, and 2) whether or not you can assume a parametric distribution for your data.

Censoring - Life data are often censored or incomplete in some way. Suppose you are testing how long a certain part lasts before wearing out and plan to cut off the study at a certain time. Any parts that did not fail before the study ended are censored, meaning their exact failure time is unknown. In this case, the failure is known only to be "on the right," or after the present time. This type of censoring is called right censoring. Similarly, all you may know is that a part failed before a certain time (left censoring), or within a certain interval of time (interval censoring).

·    Use the right-censoring commands when you have exact failures and right censored data.

·    Use the arbitrary-censoring commands when your data are arbitrarily censored to include both exact failures and a varied censoring scheme, including right-censoring, left-censoring, and interval-censoring.

For details on creating worksheets for censored data, see Distribution Analysis Data.

Distribution - Life data can be described using a variety of distributions. Once you have collected your data, you can use the commands in this chapter to select the best distribution to use for modeling your data, and then estimate the variety of functions that describe that distribution. These methods are called parametric because you assume the data follow a parametric distribution. If you cannot find a distribution that fits your data, Minitab provides nonparametric estimates of the same functions.

·    Use the parametric distribution analysis commands when you can assume your data follow a parametric distribution.

·    Use the nonparametric distribution analysis commands when you cannot assume a parametric distribution.