Basic Analysis and Graphing - SAS

56 Performing Univariate Analysis Chapter 2
Fit Distributions
Table 2.15 Description of the Capability Analysis Options
Z Bench
Capability Animation
Shows the values (represented by Index) of the Benchmark Z statistics.
According to the AIAG Statistical Process Control manual, Z represents the
number of standard deviation units from the process average to a value of
interest such as an engineering specification. When used in capability
assessment, Z USL is the distance to the upper specification limit and Z LSL
is the distance to the lower specification limit. See “Statistical Details for
Capability Analysis” on page 73.
Interactively change the specification limits and the process mean to see the
effects on the capability statistics. This option is available only for capability
analyses based on the Normal distribution.
Related Information
• “Statistical Details for Capability Analysis” on page 73
• “Example of Capability Analysis” on page 67
Fit Distributions
Use the Continuous or Discrete Fit options to fit a distribution to a continuous or discrete variable.
A curve is overlaid on the histogram, and a Parameter Estimates report is added to the report window. A red
triangle menu contains additional options. See “Fit Distribution Options” on page 58.
Note: The Life Distribution platform also contains options for distribution fitting that might use different
parameterizations and allow for censoring. See the Quality and Reliability Methods book.
Continuous Fit
Use the Continuous Fit options to fit the following distributions to a continuous variable.
• The Normal distribution is often used to model measures that are symmetric with most of the values
falling in the middle of the curve.
• The LogNormal distribution is often used to model values that are constrained by zero but have a few
very large values. The LogNormal distribution can be obtained by exponentiating the Normal
distribution.
• The Weibull, Weibull with threshold, and Extreme Value distributions often provide a good model for
estimating the length of life, especially for mechanical devices and in biology.
• The Exponential distribution is especially useful for describing events that randomly occur over time,
such as survival data. The exponential distribution might also be useful for modeling elapsed time
between the occurrence of non-overlapping events, such as the time between a user’s computer query
and response of the server, the arrival of customers at a service desk, or calls coming in at a switchboard.