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items on a two dimensional surface—e.g. the number of housing starts in a time
period and the corresponding purchase of plumbing supplies.
b. Bubble diagrams assume that the two values discussed in scatter diagrams also
have a third value (relative size of the bubble) that relates to the frequency or
strength of the point located on two dimensions—e.g. a study that tracks com-
binations of mortgage rate and mortgage points that must be paid by borrowers.
In this case, the size of the bubble is the frequency of the occurrence of specific
General Issues
a. Is the magnitude of a data value important relative to other data values occurring
in the same category or at the same time? (This was the case in Exhibit 2.4.) If
so, then consider Stacked and 100% Stacked graph. The Stacked graph preserves
the opportunity to compare across various time periods or categories—e.g. the
revenue contribution of 3 categories of products for 4 quarters provides not only
the relative importance of products within a quarter, but also shows how the
various quarters compare. Note that this last feature (comparison across quarters)
will be lost in a 100% Stacked graph.
b. In general, I find that 3-D graphs can be potentially distracting. The one excep-
tion is the display of multiple series of data (usually less than 5 or 6) where the
overall pattern of behavior is important to the viewer. Here a 3-D Line graph (rib-
bon graph) or an Area graph is appropriate, as long as the series do not obscure
the view of series with lesser values. If a 3-D graph is still your choice, exercise
the 3-D View options that reorient the view of the graph or point and grab a cor-
ner of the graph to rotate the axes. This may clarify the visual issues that make a
3-D graph distracting.
c. It may be necessary to use several chart types to fully convey the desired infor-
mation. Don’t be reluctant to organize data into several graphical formats; this is
more desirable than creating a single, overly complex graph.
d. Once again, it is wise to invoke a philosophy of simplicity and parsimony.
2.7 Summary
In the next chapter we will concentrate on numerical analysis of quantitative data.
Chap. 3, and the two chapters that follow, contain techniques and tools that are
applicable to the material in this chapter. You may want to return and review what
you have learned in this chapter in light of what is to come; this is good advice for
all chapters. It is practically impossible to present all the relevant tools for analysis
in a single chapter, so I have chosen to “spread the wealth” among the 7 chapters
that remain.
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