Page 334 - The Handbook for Quality Management a Complete Guide to Operational Excellence

A n a l y z e S t a g e 321

answer such questions as “Is vendor A’s material machine better than
ven dor B’s?” “Does the length of training have anything to do with the
amount of scrap an operator makes?” and so on.

How to Construct a Scatter Diagram
1. Gather several paired sets of observations, preferably 20 or more. A
paired set is one where the dependent variable can be directly tied
to the independent variable.
2. Find the largest and smallest independent variable and the largest
and smallest dependent variable.
3. Construct the vertical and horizontal axes so that the smallest and
largest values can be plotted.
4. Plot the data by placing a mark at the point corresponding to each
X–Y pair. If more than one classification is used, you may use dif-
ferent symbols to represent each group.
Example of a Scatter Diagram. The orchard manager has been keeping track
of the weight of peaches on a day-by-day basis. The data is collected in
pairs as shown in Table 15.1, so that for each peach its weight and the
number of days on the tree were recorded.
The independent variable, X, is the number of days the fruit has been
on the tree. The dependent variable, Y, is the weight of the peach. The
scatter diagram is shown in Fig. 15.4.

Pointers for Using Scatter Diagrams
• Scatter diagrams display different patterns that must be inter-
preted; Fig. 15.5 provides a scatter diagram interpretation guide.

• Be sure that the independent variable, X, is varied over a suffi-
ciently large range. When X is changed only a small amount, you
may not see a correlation with Y even though the correlation really
does exist.
• If you make a prediction for Y for an X value that lies outside of the
range you tested, be advised that the prediction is highly question-
able and should be tested thoroughly. Predicting a Y value beyond
the X range actually tested is called extrapolation.
• Keep an eye out for the effect of variables not included in the anal-
ysis. Often, an uncontrolled variable will wipe out the effect of
your X variable. It is also possible that an uncontrolled variable
will be causing the effect and you will mistake the X variable you
are controlling as the true cause. This problem is much less likely
to occur if you choose X levels at random. An example of this is
our peaches. It is possible that any number of variables changed

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