By Bhisham C. Gupta and H. Fred Walker
This booklet specializes in statistical quality controls (SQC), and covers such subject matters as: sampling, technique set-up/verification and pre-control, regulate charts for variables and attributes, cumulative sum and exponentially weighted relocating standard keep an eye on charts, approach strength indices, size platforms research, and popularity sampling. tips is usually given at the use of Minitab and JMP in doing those a variety of SQC purposes. Examples and pattern difficulties from all industries seem during the booklet to assist a eco-friendly Belt's comprehension of the fabric
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Additional resources for Statistical Quality Control for the Six Sigma Green Belt
Example text
Most of the decisions regarding government policies, marketing (including trade), and manufacturing are based on the outcomes of various samplings conducted in different fields. The particular type of sampling used for a given situation depends on factors such as the composition of the population and the objectives of the sampling as well as time and budget availability. Because sampling is an integral part of SQC, this chapter focuses on the various types of sampling, estimation problems, and sources of error.
A third kind of sampling design is systematic random sampling. This procedure is the easiest one, and it is particularly useful in manufacturing processes where the sampling is done on line. Under this scheme, the first item is randomly selected, and thereafter every mth item manufactured is selected until we have a sample of desired size. Systematic random sampling is not only easy to select, but under certain conditions it is also more precise than simple random sampling. indd 14 3/8/07 5:31:10 PM Elements of a Sample Survey 15 The fourth and last sampling design we will consider here is cluster random sampling.
Clearly, the longevity of such parts may vary from region to region depending on where the parts are manufactured. This could be due to differences in raw materials, training of the workers, or differences in machines. In such cases where the population is heterogeneous, we get more precise estimates by using a sample design known as stratified random sampling. In stratified random sampling, the population is divided into different nonoverlapping groups, called strata, such that they constitute the whole population; the population is as homogeneous as possible within each stratum but varies between strata.