Customize the probability plot

Click the graph to select it, then click the plus sign beside the graph and select items to display on the graph. The following information describes some of the items on the Graph Elements menu.

Scale Type

You can select one of the following types for the y-scale.

Values on the y-axis represent estimated cumulative percentages. The estimated cumulative percentage is equal to the estimated cumulative probability multiplied by 100.


Values on the y-axis represent estimated cumulative probabilities. The cumulative probability for a value x is the probability that a random observation that is taken from the population will be less than or equal to x.

Minitab uses the median rank method (also called the Benard method) to estimate the cumulative probability (r) for each observation:

In this formula, i is the rank of the observation in the sample and n is the total number of observations in the sample. For the smallest value in the sample, i = 1 and for the largest value in the sample, i = n.


Values on the y-axis represent inverse cumulative probabilities.

The score values for the normal distribution and the lognormal distribution are the inverse cumulative probability of r, calculated using the standard normal distribution.

The score values for the exponential distribution and the Weibull distribution are calculated as LN(−LN(1−r)), where LN is the natural log function.

Distribution Fit

Add a fitted distribution line to assess whether your data follow a specific theoretical distribution. For example, many statistical analyses assume that data follow a normal distribution. Data that fit the distribution well have bars that closely follow the fit line. Minitab also estimates the distribution parameters from your sample and conducts an Anderson-Darling test to test whether your data fit the theoretical distribution.

Choose a distribution

The following distributions are available.

To fit a lognormal distribution, an exponential distribution, or a Weibull distribution, all data values must be greater than 0.

The normal distribution is the most common statistical distribution because approximate normality occurs naturally in many physical, biological, and social measurement situations. Many statistical analyses assume that the data come from approximately normally distributed populations. For more information, go to Normal distribution.
A random variable follows the lognormal distribution if the logarithm of the random variable is normally distributed. Use the lognormal distribution when random variables are greater than 0. The lognormal distribution is used for reliability analysis and in financial applications, such as modeling stock behavior. For more information, go to Lognormal distribution.
Use the exponential distribution to model the time between events in a continuous Poisson process. Independent events are assumed to occur at a constant rate. For more information, go to Exponential distribution.
The Weibull distribution is a versatile distribution that can be used to model a wide range of applications in engineering, medical research, quality control, finance, and climatology. For example, the distribution is frequently used with reliability analyses to model time-to-failure data. The Weibull distribution is also used to model skewed process data in capability analysis. For more information, go to Weibull distribution.
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