  In preliminary analysis of control charts, one may encounter multiple shifts
and/or outliers especially with a large number of observations. The following
paper addresses this problem. A statistical model for detecting and estimating
multiple change points in a finite batch of retrospective (phase I)data is
proposed based on likelihood ratio test. We consider a univariate normal
distribution with multiple step shifts occurred in predefined locations of
process mean. A numerical example is performed to illustrate the efficiency of
our method. Finally, performance comparisons, based on accuracy measures and
precision measures, are explored through simulation studies.
