TABLE OF CONTENTS Plotting process data is an important and necessary step in understanding process variation. Both simple and complex techniques can be used. In this chapter, we discuss two techniques for analyzing data collected over time. The first is a very simple technique, called a time plot, and the second, more elaborate one, is called a variogram. More sophisticated statistical time series tools can be used and are often advantageous, and they should be considered if a more theoretical understanding is indicated. Since our intent here is to present basics, we will not discuss any advanced procedures and will give only an introduction to the variogram.
We have seen that two sources of sampling error are the variation of the material as a short-range or localized phenomenon: the FE, and the GSE. [12] Variations, such as cycles, long-range trends, and nonrandom changes, result from differences in the material over time. Changes in the process, either intentional or incidental, result in variation, and samples taken sufficiently far apart in time may differ from each other substantially in the properties of interest. If we do not characterize the process variation relative to the material variation, our ability to understand and control the process or to reduce its variation will be limited and, in some cases, futile.
We examined the fundamentals of one-dimensional sampling in the previous chapter, and we can characterize variation in time as one-dimensional. A onedimensional stream in...
