PROBLEM SET 4 I worked with Ayato Okuda Chapter 14 CONTROL...

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PROBLEM SET 4 I worked with: Ayato Okuda Chapter 14: CONTROL CHARTS These problems are the problems in Chapter 14 that is an electronic chapter available in Resources on Laulima Individual and Group Work When interpreting a control chart take care here to tell a story! Don’t just look for patterns and state things like “in-control rule 1”. The key here is to use the control chart as evidence that a process is behaving as expected or needs review. Keep in mind that “out-of-control” does not necessarily mean the process is bad or needs review . In some cases you may expect the process to be out-of-control. For example if you just introduced a major change in the process to improve an in-control process you would certainly hope to see it out-of-control in the way you expect the change to effect the process. So patterns ≠ out-of-control, and no-patterns ≠ in-control. The patterns are just “indicators” of expected and un-expected process events. Several “nearly unexpected” patterns together may indicate something highly unexpected and out-of-control. Don’t try to be too precise about the patterns. For example, when you see a point “on the UCL line” do you consider this as “over the UCL” or not? This cannot be answered without looking at the context of the other points. For example, is it part of an unexpected upward trend (even if it’s not a trend of 7 points)? What might explain this trend or it is just normal (or expected) variation? Be sure to take note of “good” patterns from “bad” patterns. For example in many p-charts, a low proportion is interpreted as “good” so a trend downward is actually good for the process even if technically it means it’s out- of-control. 1. Do problem 14.21 on page 25 (.5 point). a. Special or “assignable” causes of variation, or represent large patterns in data that are not part of a certain process. The fluctuations are caused by unusual events and can either represent problems to correct or opportunities to exploit. Common or “chance” causes of variation, on the other hand, represent the inherent variability that exists in a process. Small causes of variability that operate randomly or by chance cause these fluctuations. 2. Do problem 14.25 (.5 point). a. Variable control charts are used to monitor and analyze a process when you have numerical data such as time, money, and weight. Variable control charts are more sensitive than the p chart in detecting special cause variation. Attribute control charts are used for categorical or discrete variables. 3. Do problem 14.26 (.5 point). a. The R chart is the simplest and most common control chart, and is only used when he sample size is 10 or less. It enables you to determine whether the variability in a process is in control or changes over time. The R chart connects with the Mean Chart because if the process range is in control, the variation in the process is consistent over time, and the results of the R chart can develop the control limits for the Mean Chart.
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  • Fall '08
  • Watson-perreira,T
  • History, UCL

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