Chapter 2: Graphs, Charts, and Tables--Describing Your Data

32 CHAPTER 2 • GRAPHS, CHARTS, AND TABLES—DESCRIBING YOUR DATA
W HY Y OU N EED TO K NOW
We live in an age where we are constantly bombarded with
visual images and stimuli. Much of our time is spent watching
television, playing video games, or working at a computer
monitor. These technologies are advancing rapidly, making
the images sharper and more attractive to our eyes. Flat-panel
screens, high-resolution monitors, and high-definition televisions
represent significant improvements over the original
technologies that they replaced. However, this phenomenon
is not limited to video technology, but has also become an
important part of the way businesses communicate with customers,
employees, suppliers, and other constituents.
Presentations and reports are expected to include
high-quality graphs and charts that effectively transform
data into information. While the written word is still vital,
words become even more powerful when coupled with an
effective visual illustration of data. The adage that “a picture
is worth a thousand words” is particularly relevant in
business decision making.
As a business major, upon graduation you will find
yourself on both ends of the data analysis business. On the
one hand, regardless of what you end up doing for a career,
you will almost certainly be involved in preparing reports
and making presentations requiring the use of the visual
descriptive statistical tools presented in this chapter. You
will be on the “do it” end of the data analysis process.
Thus, you need to know how to use these statistical tools.
On the other hand, you will also find yourself reading
reports or listening to presentations that others have made.
In many instances, you will be required to make important
decisions, or reach conclusions, based on the information
in those reports or presentations. Thus, you will be on the
“use it” end of the data analysis process. You need to be
knowledgeable about these tools in order to effectively
screen and critique the work that others do for you.
Charts and graphs are not just tools used internally by
businesses. Business periodicals such as Fortune and
Business Week use graphs and charts extensively in articles
to help readers better understand key concepts. Many
advertisements will even use graphs and charts effectively
to convey their message. Virtually every issue of The Wall
Street Journal contains different graphs, charts, or tables
that display data in an informative way.
Thus, you will find yourself as both a producer and a
consumer of the descriptive statistical techniques known as
graphs, charts, and tables. You will create a competitive
advantage for yourself throughout your career if you
obtain a solid understanding of the techniques introduced
in Chapter 2.
This chapter introduces some of the most frequently used tools and techniques for
describing data with graphs, charts, and tables. Although this analysis can be done manually,
we will provide output from Excel and Minitab showing that these software packages
can be used as tools for doing the analysis easily, quickly, and with a finished quality that
once required a graphic artist.
CHAPTER OUTCOME #1
Frequency Distribution
A summary of a set of data that
displays the number of
observations in each of the
distribution’s distinct categories
or classes.
Discrete Data
Data that can take on a countable
number of possible values.
2.1 Frequency Distributions and Histograms
As we discussed in Chapter 1, in today’s business climate, companies collect massive
amounts of data they hope will be useful for making decisions. Every time a customer
makes a purchase at a store like Wal-Mart or Sears, data from that transaction is updated to
the store’s database. For example, one item of data that is captured is the number of different
product categories included in each “market basket” of items purchased. Table 2.1
shows these data for all customer transactions for a single day at one store in Atlanta. A total
of 450 customers made purchases on the day in question. The first value, 4, in Table 2.1
indicates that the customer’s purchase included four different product categories (for example
food, sporting goods, photography supplies, and dry goods).
While the data in Table 2.1 are easy to capture with the technology of today’s cash
registers, in this form the data provide little or no information that managers could use to
determine the buying habits of their customers. However, these data can be converted into
useful information through descriptive statistical analysis.
Frequency Distribution
One of the first steps would be to construct a frequency distribution.
The product data in Table 2.1 take on only a few possible values (1, 2, 3, ..., 11). The
minimum number of product categories is 1 and the maximum number of categories in
these data is 11. These data are called discrete data.