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If the process mean drifts between several levels during the interval between samples, the range of observations within the sample may consequently be relatively large It is the within-sample variability that determines the width of the control limits on an X chart, so this practice will result in wider limits on the X chart This makes it harder to detect shifts in the mean In fact, we can often make any process appear to be in statistical control just by stretching out the interval between observations in the sample It is also possible for shifts in the process average to cause points on a control chart for the range or standard deviation to plot out of control, even though no shift in process variability has taken place There are other bases for forming rational subgroups.

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and Jordan [681 originally exploited this analogy to quantize the radiation field in the 194Os, but the oscillator approach only became really popular as a simple way to get to QED when it appeared in Fermi's review paper.

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For example, suppose a process consists of several machines that pool their output into a common stream If we sample from this common stream of output, it will be very dif cult to detect whether or not some of the machines are out of control A logical approach to rational subgrouping here is to apply control chart techniques to the output for each individual machine Sometimes this concept needs to be applied to different heads on the same machine, different workstations, different operators, and so forth The rational subgroup concept is very important The proper selection of samples requires careful consideration of the process, with the objective of obtaining as much useful information as possible from the control chart analysis..

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from your site and tell Google to ignore those parameters when it crawls. For example, if your site has URLs /authors.php and /authors.php sortby=lastname, you could tell Google to ignore the sortby parameter, which would help canonicalize any URLs that contain that parameter back to /authors.php. Google also lists dynamic URL parameters that Google suspects might be meaningless enough to create duplicate content on your site, but honestly, Google doesn t bat too well on this, often suggesting important parameters that create unique content.

A control chart may indicate an out-of-control condition either when one or more points fall beyond the control limits, or when the plotted points exhibit some nonrandom pattern of behavior. For example, consider the X chart shown in Fig. 16-4. Although all 25 points fall within the control limits, the points do not indicate statistical control because their pattern is very nonrandom in appearance. Speci cally, we note that 19 of the 25 points plot below the center line, while only 6 of them plot above. If the points are truly random, we should expect a more even distribution of them above and below the center line. We also observe that following the fourth point, ve points in a row increase in magnitude. This arrangement of points is called a run. Since the observations are increasing, we could call it a run up; similarly, a sequence of decreasing points

(A36) (A37)

is called a run down This control chart has an unusually long run up (beginning with the fourth point) and an unusually long run down (beginning with the eighteenth point) In general, we de ne a run as a sequence of observations of the same type In addition to runs up and runs down, we could de ne the types of observations as those above and below the center line, respectively, so two points in a row above the center line would be a run of length 2 A run of length 8 or more points has a very low probability of occurrence in a random sample of points Consequently, any type of run of length 8 or more is often taken as a signal of an out-of-control condition For example, eight consecutive points on one side of the center line will indicate that the process is out of control.

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Although runs are an important measure of nonrandom behavior on a control chart, other types of patterns may also indicate an out-of-control condition For example, consider the X chart in Fig 16-5 Note that the plotted sample averages exhibit a cyclic behavior, yet they all fall within the control limits Such a pattern may indicate a problem with the process, such as operator fatigue, raw material deliveries, and heat or stress buildup The yield may be improved by eliminating or reducing the sources of variability causing this cyclic behavior (see Fig 16-6) In Fig 16-6, LSL and USL denote the lower and upper speci cation limits of the process These limits represent bounds within which acceptable product must fall and they are often based on customer requirements.

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