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Control Charts for Attribute Data
p-charts for proportion nonconforming units, sample size not necessarily constant
¬  When to use a p-chart
• •  When it is difficult or uneconomical to make a numerical measurement
• •  When it is desired to combine different types of defects into an overall proportion
• •  When the available data are for attributes
• •  When the data come from a binomial process
¬  Management summaries
• •  Many management summaries are attribute forms and could benefit from control chart analysis.
• •  Examples: scrap rates, quality audits, first-run yields, etc.
np-charts for number of nonconforming units, sample size is constant
¬  When to use an np-chart?
• •  Use the same criteria as a p-chart:
•      (a)  When it is difficult or uneconomical to make a numerical measurement
•      (b)  When it is desired to combine different types of defectives into a single value
•      (c)  When the available data are for attributes
•      (d)  When the data come from a binomial process
• •  And the subgroups are all the same size
• ¬  How is it different from a p-chart?
• •  The actual number of nonconforming parts are plotted, rather than the proportion defective
u-chart for the number of defects per inspection unit, sample size not necessarily constant
c-chart for the number of defects, sample size constant
¬  When to use c- or u-charts
• •  When the data is attribute data of defects, not defectives
• •  When the data comes from a Poisson process
• •  c-charts are charts constructed for number of occurrences for a constant exposure
• •  u-charts are constructed for rate of occurrence for either constant or varying exposure
¬  Examples
• •  Each month, 100 invoices are audited and the total number of mistakes is recorded.
• •  In a molding process, there is a problem with pinholes in plastic bottles. Each day, a number of bottles are examined and the number of pinholes are recorded.<
• •  Each month, the number of accidents that occur in an organization is recorded.
Control Charts for Variable Data
X-R Chart
¬  The X-R chart is the most versatile of control charts, and is used in most applications.
¬  Charting of averages and charting of ranges are used to check if a constant-cause system exists.
• •  X-chart measures variability between samples
• •  R-chart measures variability within samples
• •  For sample size n > 10, R loses its efficiency in estimating process sigma and R-chart may not be appropriate.
X-S Chart
¬  The S Chart may be used when n is not constant
¬  For large sample size (n 10), the range loses its efficiency as an estimator of
The I-MR Chart is a useful control chart if the characteristic is independently and normally distributed. Applications where sample size for process monitoring is n=1.
¬  Measurement is expensive, e.g. destructive testing
¬  Production rate is very slow
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