Modeling Manufacturing Data with a Doubly Truncated Logistic
by Mary L. Baggett
.
Abstract:
Statistical tolerance analysis typically assumes data
from manufacturing processes have a normal distribution. Processes
from a wide variety of industries have output with skewed
distributions or finite ranges. One method of coping with this
problem has been to use the beta distribution to model
manufacturing data. The use of a doubly truncated logistic
distribution for this situation is examined. This distribution is
suitable for a wider class of processes than the beta
distribution. Parameter estimation for this distribution is more
straightforward than it would be for a truncated normal
distribution. Comparisons are made between the use of the normal
and truncated logistic distributions for predicting the proportion
of product that fails to meet requirements using actual
manufacturing data.
Key Words:
Truncated Logistic Distribution, Quality Improvement,
Manufacturing Data
Authors:
Mary L. Baggett,
mbaggett@olemiss.edu
Editor:
Ramon V. Leon
,
rleon@utk.edu
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