An empirical CDF plot is a graph that you can use to evaluate the fit of a distribution to your data, estimate percentiles, and compare different sample distributions. An empirical CDF plot does the following:
Plots each unique value vs the percentage of values in the sample that are less than or equal to it, and connects the points with a stepped line.
Fits a cumulative distribution function (CDF) for the selected distribution so that you can examine how well the distribution fits your data.
Displays a table with the distribution parameter estimates and the number of observations (N) for your data.
If you hold the pointer on a point on the stepped line, Minitab displays the row number and x- and y-values for the point. If you hold the pointer on the fitted line, Minitab displays the estimated percentiles for multiple points.
An empirical CDF plot performs a similar function as a probability plot. However, unlike a probability plot, the empirical CDF plot has scales that are not transformed and the fitted distribution does not form a straight line.
This empirical CDF plot reveals the following:
The normal distribution seems to fit the sample; therefore, the manager can use the fitted line to estimate percentiles. If it hadn't fit, the manager could try fitting CDF lines for other distributions.
The mean wait time is 3.573; the standard deviation is 0.5700.
It seems that about 80% of the data fall below 4.0. The manager can hold the pointer on the CDF line to get exact numbers. The manager could also add a percentile line at 4 minutes.
The plot shows observations from only one restaurant. The manager could also display and compare data for several restaurants on the same empirical CDF plot.