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

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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.
CHAPTER 2 • GRAPHS, CHARTS, AND TABLES—DESCRIBING YOUR DATA 33 TABLE 2.1 Product Categories Per Customer at the Atlanta Retail Store 4 2 5 8 8 10 1 4 8 3 4 1 1 3 4 1 4 4 5 4 4 4 9 5 4 4 10 7 11 4 10 2 6 7 10 5 4 6 4 6 2 3 2 4 5 5 4 11 1 4 1 9 2 4 6 6 7 6 2 3 6 5 3 4 5 6 5 3 10 6 5 7 7 4 3 8 2 2 6 5 11 9 9 5 5 6 5 3 1 7 6 6 5 3 8 4 3 3 4 4 4 7 6 4 9 1 6 5 5 4 4 7 5 6 6 9 5 6 10 4 7 5 8 4 4 7 4 6 6 4 4 2 10 4 5 4 11 8 7 9 5 6 4 2 8 4 2 6 6 6 6 4 6 5 7 1 6 9 1 5 9 10 5 5 10 5 4 7 5 7 6 9 5 3 2 1 5 5 5 5 5 9 5 3 2 5 7 2 4 6 4 4 4 4 4 6 5 8 5 5 5 5 5 2 5 5 6 4 6 5 5 7 10 2 2 6 8 3 1 3 5 6 3 3 6 5 4 5 3 3 7 9 4 4 5 10 6 10 5 9 4 3 8 7 1 8 4 3 1 3 6 7 5 5 5 4 7 4 11 6 6 3 7 9 4 4 2 9 7 5 1 6 6 8 3 8 4 4 1 9 3 9 3 4 2 9 5 5 7 10 5 3 4 7 7 6 2 2 4 4 4 7 3 5 4 9 2 3 4 3 2 1 6 4 6 1 8 1 4 3 5 5 10 4 4 4 6 9 2 7 9 4 5 3 6 5 5 3 4 6 5 7 3 6 8 3 6 1 5 7 7 5 4 6 6 6 3 6 9 5 4 5 10 1 5 5 7 8 9 1 6 5 6 6 4 10 6 5 5 5 1 6 5 6 4 7 9 10 2 6 4 4 6 11 9 5 4 4 3 5 4 6 2 6 7 3 5 6 7 4 5 4 6 9 4 3 3 6 9 4 3 7 5 6 11 4 4 8 4 2 8 2 4 2 3 6 5 1 10 5 9 5 4 5 1 4 9 5 4 4 When you encounter discrete data, where the variable of interest can take on only a reasonably small number of possible values, a frequency distribution is constructed by counting the number of times each possible value occurs in the data set. We organize these counts into a frequency distribution table as shown in Table 2.2. Now, from this frequency distribution we are able to see how the data values are spread over the different number of possible product categories. For instance, you can see that the most frequently occurring number of product categories in a customer’s “market basket” is 4, which occurred 92 times. You can also see that the three most common number of product categories are 4, 5, and 6. Only a very few times do customers purchase 10 or 11 product categories in their shopping trip to the store. Consider another example in which a consulting firm surveyed random samples of residents in two cities, Dallas, Texas, and Knoxville, Tennessee. The firm is investigating the labor markets in these two communities for a client that is thinking of relocating its corporate offices to one of the two locations. Education level of the workforce in the two cities is a key factor in making the relocation decision. The consulting firm surveyed 160 randomly selected adults in Dallas and 330 adults in Knoxville and recorded the number
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