Capability Analysis (Normal Distribution)overview how to examples data see also
Capability Analysis (Normal Distribution)Stat > Quality Tools > Capability Analysis > Normal
Use Capability Analysis (Normal) to produce a process capability report when your data are from a normal distribution or to transform the data to follow a normal distribution using a Box-Cox or Johnson transformation.
The report includes a capability histogram overlaid with two normal curves, and a complete table of overall and within capability statistics. The two normal curves are generated using the process mean and within standard deviation and the process mean and overall standard deviation.
The report also includes statistics of the process data, such as the process mean, the target (if you enter one), the within and overall standard deviation, and the process specifications; the observed performance; and the expected within and overall performance. So the report can be used to visually assess whether the data are normally distributed, whether the process is centered on the target, and whether it is capable of consistently meeting the process specifications.
A model that assumes the data are from a normal distribution suits most process data. If your data are very skewed, see the discussion under nonnormal data.
Data are arranged as
Single column: Choose if data is in one column. Enter a column.
Subgroup size (use a constant or an ID column): Enter the subgroup size (for equal-size subgroups) or a column of subscripts (for unequal-size subgroups).
Subgroups across rows of: Choose if subgroups are arranged in rows across several columns. Enter the columns.
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If you select the Johnson transformation, subgroup sizes are not used. |
Lower spec: Enter the lower specification limit. Check Boundary to define the number as a "hard" limit. See Upper spec below for an explanation.
Upper spec: Enter the upper specification limit. Check Boundary to define the number as a "hard" limit, meaning that it is impossible for a measurement to fall outside the limit.
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You must enter a lower specification limit and/or an upper specification limit. When you define upper and lower specification limits as boundaries, Minitab sets the expected percentage of values that are out of specifications to * for a boundary. |
Historical mean (optional): Enter a value for the mean of the population distribution if you have a known process parameter or an estimate obtained from past data. If you do not specify a value for the mean, it is estimated from the data.
Historical standard deviation (optional): Enter a value for standard deviation of the population distribution if you have a known process parameter or an estimate obtained from past data. If you do not specify a value for standard deviation, it is estimated from the data. Minitab can estimate standard deviation in one of three ways for sample sizes larger than 1, or one of three ways if you have a subgroup size of 1. See <Estimate>.
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You cannot use historical parameters if you transform the data. |