X-bar and R charts

# X-bar and R charts - difference exists is then about 1 out...

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X-bar and R charts Example 3.1 from text

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Data on part thickness Thickness of parts recorded as amount by which  thickness exceeded 0.300 in. (everyone else has gone  metric but……..)
Data structure Sample  Value 1    1 1    4 1    6 1    4 2    3 2    7 2    5 2    5

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Raw data plot, thickness vs  sample number
Table 3.2 constants for X-bar  and R charts

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Rules for creating charts
Rules, etc.

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Limits are designed to …. Make sure that you do not react to Common cause. Indicate when you are reasonably sure that Special  Cause is present. If only Common Cause is present in the process,  then the chance of a false signal is about 1%, i.e. the  probability that the chart will falsely indicate the  presence of Special Cause is about 0.01.
Similar to Hypothesis Testing In hypothesis testing we say there is a treatment  difference if p<alpha=0.05 (usually). The chance of falsely declaring a treatment

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Unformatted text preview: difference exists is then about 1 out of 20. In Quality Control, we use 1 out of 100 to say that our process has more variation than just Common Cause. R-chart and Common Cause If the data in each subgroup was collected under “homogeneous conditions”, then the Ranges should reflect only Common Cause. The chart should not indicate the presence of Special Cause. R chart X-bar chart and Special Cause If the R-chart is in control, i.e., stable and predictable, then any shifts in the mean of the process come from Special Cause. If the X-bar chart indicates the process is “out of control”, i.e., that Special Cause is present. We then use a fishbone diagram or Cause and Effect Matrix to try to identify and remove the source of Special Cause. X-bar chart...
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