Page 293 - Six Sigma Demystified

Part 3 s i x s i g m a to o l s 273

Histogram

Minitab

Use Stat\Quality Tools\Capability Analysis.

Excel

Using Green Belt XL Add-On
Use New Chart\Histogram. (Note: The histogram also may be displayed as an
option with the X and individual-X control charts.)

Interpretation

An advantage of a histogram is that the process location is clearly identifiable.
In Figure F.17, the central tendency of the data is about 75.005. The variation
is also clearly distinguishable: We expect most of the data to fall between
75.003 and 75.007. We also can see if the data are bounded or if they have
symmetry, such as is evidenced in these data.
If your data are from a symmetric distribution, such as the bell-shaped
normal distribution shown in Figure F.17A, the data will be evenly distrib-
uted about the center of the histogram. If the data are not evenly distributed
about the center of the histogram, the histogram is skewed. If it appears
skewed, you should understand the cause of this behavior. Some processes

naturally will have a skewed distribution and also may be bounded, such as
the concentricity data in Figure F.17B. Concentricity has a natural lower
bound at zero because no measurements can be negative. Most of the data
are just above zero, so there is a sharp demarcation at the zero point repre-
senting a bound.
If double or multiple peaks occur, look for the possibility that the data are
coming from multiple sources, such as different suppliers or machine
adjustments.
The histogram provides a view of the process as measured. The actual output
over a larger sample period may be much wider, even when the process is in
control. As a general rule, 200 to 300 data observations are preferred to provide
a realistic view of a process distribution, although it is not uncommon to use a