Capability analysis
compares the distribution of a process to its specification limits.
When you run the Capability Analysis task, the output includes a variety
of statistics for summarizing the data distribution of the process
variable. Examples of statistics are sample moments, basic statistical
measures, and quantiles.
When you add specification
limits, the output includes statistics such as percents of measurements
within and outside the specification limits and process capability
indices.
You can plot the results
by using a histogram, a probability plot, and a quantile-quantile
plot.
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Histograms are typically used in
process capability analysis to compare the distribution of measurements
from an in-control process to its specification limits.
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A probability plot compares ordered
values of a variable with percentiles of a specified theoretical distribution
such as the normal. If the data distribution matches the theoretical
distribution, the points on the plot form a linear pattern. Thus,
you can use a probability plot to determine how well a theoretical
distribution models a set of measurements.
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A quantile-quantile plot (Q-Q plot)
compares ordered values of a variable with quantiles of a specified
theoretical distribution such as the normal. If the data distribution
matches the theoretical distribution, the points on the plot form
a linear pattern. Thus, you can use a Q-Q plot to determine how well
a theoretical distribution models a set of measurements.