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Statistical process control
 Takes periodic samples from process
 Plots samples on control chart
 Determine if its within the limits
 Correct process before defects are produced

Attribute data
product characteristics that have discrete choice color feel taste

Variable data
 Product characteristics that can be measured
 Length, size, weight

Control charts
used to monitor the quality of manufacturing process

"in control"
 There are no sample points outside control limits
 Most points are near the process center line, without too many close to the control limits
 Approximately equal numbers of sample points occur above and below the center line
 The points appear to be randomly distributed around the center line

P chart
 Calculate percent defectives in a sample
 based on binomial distribution

C chart
 count number of defects in an item
 based on poisson distribution

Mean chart (Xbar Chart)
Measure the central tendency of a sample

Range chart (R chart)
 The difference between the highest and lowest value
 measure the amount of dispersion in a sample

Central Limit Theorem
if you graph the means of a series of observations, the data will approach a normal distribution

Process Capability
 Can it meet design specifications
 Natural variability in a process
 3 sigma quality  specifications equal to the process control limits
 6 sigma quality  specifications are twice as large as the control limits

Cp
 Process capability ratio
 Tolerance range / process range
 Cp<1 not capable
 Cp = 1 just capable
 Cp > 1 process is capable
 Cp = 2 six sigma quality

Cpk
 Measures if process is off center
 Process capability index
 Cpk = Cp, the process is exactly on center
 Cpk < 0, process is completely outside the design specs

