city of Los Angeles, which is infamous for its traffic. These data are from Data Set 31 “Commute Times” in Appendix B. It is an exceptionally rare person who can simply look at the data in Table 2-1 and form meaningful conclusions. We mere mortals must work at describing, exploring, and comparing such data to gain meaningful insights. In this chapter we present methods that focus on organizing and summarizing the data and using graphs that enable us to understand important characteristics of the data, especially the distribution of the data. This chapter and the following chapter focus on important characteristics of data, including the following: Important Characteristics of Data 1. Center: A representative value that shows us where the middle of the data set is located. 2. Variation: A measure of the amount that the data values vary. 3. Distribution: The nature or shape of the spread of the data over the range of values (such as bell-shaped). 4. Outliers: Sample values that lie very far away from the vast majority of the other sample values. 5. Time: Any change in the characteristics of the data over time. This chapter provides tools that enable us to gain insight into data by organizing, summarizing, and representing them in ways that enable us to see important characteristics of the data. Here are the chapter objectives: 2-1 Frequency Distributions for Organizing and Summarizing Data • Develop an ability to summarize data in the format of a frequency distribution and a relative frequency distribution. • For a frequency distribution, identify values of class width, class midpoint, class limits, and class boundaries. 2-2 Histograms • Develop the ability to picture the distribution of data in the format of a histogram or relative frequency histogram. • Examine a histogram and identify common distributions, including a uniform distribution and a normal distribution. 2-3 Graphs That Enlighten and Graphs That Deceive • Develop an ability to graph data using a dotplot, stemplot, time-series graph, Pareto chart, pie chart, and frequency polygon. • Determine when a graph is deceptive through the use of a nonzero axis or a pictograph that uses an object of area or volume for one-dimensional data. CHAPTER OBJECTIVES TABLE 2-1 Daily Commute Time (minutes) in Los Angeles 18254575604025 8 5010103015255020302045306030201530 6030153540 5 304020104530152525 5 903015602060302525 HINT Remember the sentence “Computer Viruses Destroy Or Terminate” to recall the first letters of the characteristics (CVDOT). 44 CHAPTER 2 Exploring Data with Tables and Graphs
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