ABSTRACT
The SPC control charts discussed in the previous several chapters, for monitoring
univariate or multivariate continuous numerical quality characteristics, are all based
on the assumption that the related quality characteristics follow normal distributions
when the production process in question is IC and after it becomes OC. As pointed
out in Subsection 2.3.1, normal distributions play an important role in statistics, be-
cause many continuous numerical variables in practice roughly follow normal dis-
tributions and much statistical theory is developed for normally distributed random
variables. An intuitive explanation about the reason many continuous numerical vari-
ables in our daily life roughly follow normal distributions can be given using the
central limit theorem (CLT) discussed in Subsection 2.7.1. For instance, a quality
characteristic in question (e.g., the lifetime of a machine) is often affected by many
different factors, including the quality of the raw material, labor, manufacturing fa-
cilities, proper operation in the manufacturing process, and so forth (cf., Figure 1.1
in Section 1.2). So, by the CLT, its distribution would be roughly normal.