Download - X-bar and R charts
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X-bar and R chartsExample 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 elsehas one metric but!!.."
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Data structure#ample $alue
1 1
1 %
1 &1 %
' 3
'
' )
' )3 %
3 )
3 )
3 * etc.
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+aw data plot* thickness vs
sample number
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Table 3.' constants for ,-bar and + charts
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+ules for creatin charts
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+ules* etc.
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imits are desined to !./ake sure that the operator does not react to
ommon cause.
ndicate when you are reasonably sure that#pecial ause is present.
f only ommon ause is present in theprocess* then the chance of a false sinal is
about 12* i.e. the probability that the chartwill falsely indicate the presence of #pecialause is about 0.01.
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#imilar to ypothesis Testinn hypothesis testin we say there is a
treatment di4erence if p5alpha60.0)(usually".
The chance of falsely declarin a treatmentdi4erence exists is then about 1 out of '0.
n 7uality ontrol* we use the 81 out of 1009
criteria to say that our process has morevariation than :ust ommon ause (idea dueto #hewhart* it is simple but e4ective inpractice".
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+-chart and ommon ausef the data in each subroup was collected
under 8homoeneous conditions9* then the+anes should re;ect only ommon ause.
The chart should not indicate the presence of#pecial ause.
f no sinal of #pecial ause is indicated* thisis not proof that within subroup variation isonly due to ommon ause. (/ore on thislater in +ational #ubroupin."
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+ chart
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,-bar chart and #pecial ause f the +-chart is in control* i.e.* stable and
predictable* then any shifts in the mean of theprocess come from #pecial ause. f the ,-barchart indicates the process is 8out of control9*i.e.* that #pecial ause is present. <e thenuse a =shbone diaram (or detailed auseand E4ect /atrix" to try to identify and
remove the source of #pecial ause.
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,-bar chart