Section 2.2

# Section 2.2

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## Section 2.2

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1. Section 2.2 Graphical Displays of Data

2. Objectives Create and interpret the basic types of graphs used to display data. Distinguish between the basic shapes of a distribution.

3. Graphs Should be able to stand alone without the original data. Must have a title and labels for both axes. When appropriate, a legend, a source, and a date should be included.

4. Pie Charts A pie chart shows how large each category is in relation to the whole. It is used to display qualitative or categorical data. A pie chart uses the relative frequencies from the frequency distribution to divide the “pie” into different-sized wedges. The size, or central angle measure, of each wedge in the pie chart is calculated by multiplying 360° by the relative frequency of each class and rounding to the nearest whole degree.

5. Example 2.7: Creating a Pie Chart Create a pie chart from the following data describing the distribution of housing types for students in a statistics class. Calculate the size of each wedge in the pie chart to the nearest whole degree.

6. Example 2.7: Creating a Pie Chart (cont.) Solution Relative Frequencies Central Angle Measures

7. Example 2.7: Creating a Pie Chart (cont.)

8. Types of Bar Graphs A bar graph uses bars to represent the amount of data in each category. Displays qualitative data. Pareto charts are bar graphs in which the bars appear in descending order from largest to smallest. This type of bar graph is typically used with nominal data. A side-by-side bar graph is used when we want to create a bar graph that compares the same categories for different groups. A stacked bar graph can be a more efficient graph for displaying data from different samples.

9. Example 2.8: Creating a Bar Graph Create a bar graph of the following data describing the distribution of housing types for students in a statistics class.

10. Example 2.9: Creating a Pareto Chart Create a Pareto chart, if appropriate, for the following data.

11. Example 2.9: Creating a Pareto Chart (cont.) Solution The data are nominal; therefore, it would be appropriate to create a Pareto chart from these data. Begin by rearranging the data from the largest frequency to the smallest frequency.

12. Example 2.9: Creating a Pareto Chart (cont.) Next, create a bar graph of the ordered data. The resulting Pareto chart is shown.

13. Example 2.10: Creating a Side-by-Side Bar Graph Create a side-by-side bar graph of the following data describing the distribution of housing types for two different samples of students.

14. Example 2.11: Creating a Stacked Bar Graph Create a stacked bar graph for the data in Example 2.10. Solution With the stacked bar graph, it is easier to see that more students live in the dorms than in apartments.

15. Histograms A frequencyhistogram, shortened to histogram, is a bar graph of a frequency distribution of quantitative data. A relative frequencyhistogram is a histogram in which the heights of the bars represent the relative frequencies of each class rather than simply the frequencies.

16. Characteristics of Histograms • A bar graph of a frequency distribution. • The horizontal axis is a real number line. • The width of the bars represent the class width from the frequency table and should be uniform. • The bars in a histogram should touch. • The height of each bar represents the frequency of the class it represents.

17. Example 2.12: Constructing a Histogram Construct a histogram of the 3-D TV prices from the previous section. The frequency distribution of the data is restated here.

18. Example 2.12: Constructing a Histogram (cont.)

19. Example 2.13: Constructing a Relative Frequency Histogram • Construct a relative frequency histogram of the 3-D TV prices from the previous section. The frequency distribution of the data is reprinted here.

20. Example 2.13: Constructing a Relative Frequency Histogram (cont.) Solution

21. Frequency Polygons A frequency polygon is a visual display created by plotting a point at the frequency of each class above each class midpoint and connecting the points using straight lines.

22. Frequency Polygons Constructing a Frequency Polygon • 1. Mark the class boundaries on the x‑axis and the frequencies on the y‑axis. Note, extra classes at the lower and upper ends will be added, each having a • frequency of 0. In our 3-D TV price example, these classes will be 1400–1499 at the lower end and 2000–2099 at the upper end.

23. Frequency Polygons Constructing a Frequency Polygon (cont.) 2.Add the midpoints to the x-axis and plot a point at the frequency of each class directly above its midpoint. Notice that the class boundaries on the x‑axis have been lightened. This is due to the fact that frequency polygons represent the midpoints of the classes.

24. Frequency Polygons Constructing a Frequency Polygon (cont.) 3.Join each point to the next with a line segment. Notice, this is not a smooth curve, but a polygon.

25. Ogive An ogive (pronounced “oh-jive”) is a line graph which depicts the cumulative frequency of each class from a frequency table. To create an ogive, begin by tabulating the cumulative frequency for each class. Unlike when creating a frequency polygon, we only include an extra class at the lower end for this graph, giving it a frequency of 0. Next, plot a point at the cumulative frequency for each class directly above its upper class boundary. The ogive is created by joining the points together with line segments.

26. Example 2.14: Creating an Ogive Below is the frequency distribution of the 3-D TV prices with the cumulative frequency column included. Create an ogive of the data.

27. Example 2.14: Creating an Ogive (cont.)

28. Example 2.14: Creating an Ogive (cont.) Solution Notice we have included the class 1400–1499 with a frequency of 0. The following is an ogive of the data.

29. Stem-and-Leaf Plots A stem-and-leaf plot is a graph of quantitative data that is similar to a histogram in the way that it visually displays the distribution. • Characteristics: • A stem-and-leaf plot retains the original data. • The leaves are usually the last digit in each data value and the stems are the remaining digits. • A legend, sometimes called a key, should be included so that the reader can interpret the information.

30. Stem-and-Leaf Plots Constructing a Stem‑and-Leaf Plot 1.Create two columns, one on the left for stems and one on the right for leaves. 2.List each stem that occurs in the data set in numerical order. Each stem is normally listed only once; however, the stems are sometimes listed two or more times if splitting the leaves would make the data set’s features clearer.

31. Stem-and-Leaf Plots Constructing a Stem‑and-Leaf Plot (cont.) 3.List each leaf next to its stem. Each leaf will be listed as many times as it occurs in the original data set. There should be as many leaves as there are data values. Be sure to line up the leaves in straight columns so that the table is visually accurate. 4.Create a key to guide interpretation of the stem‑and-leaf plot. 5.If desired, put the leaves in numerical order to create an ordered stem-and-leaf plot.

32. Example 2.15: Creating a Stem-and-Leaf Plot Create a stem-and-leaf plot of the following ACT scores from a group of college freshmen.

33. Example 2.15: Creating a Stem-and-Leaf Plot (cont.) Solution ACT Scores Stem Leaves 1 2 3 8 9 8 8 7 3 4 7 6 2 7 9 4 0 1 5 6 1 2 5 Key: 1|8 = 18

34. An Ordered Stem-and-Leaf Plot The ordered stem-and-leaf plot from Example 2.15 is as follows. ACT Scores Stem Leaves 1 2 3 7 8 8 8 9 0 1 2 3 4 4 5 6 6 7 7 9 1 2 5 Key: 1|8 = 18

35. Example 2.16: Creating and Interpreting a Stem-and-Leaf Plot Create a stem-and-leaf plot for the following starting salaries for entry-level accountants at public accounting firms. Use the stem-and-leaf plot that you create to answer the following questions.

36. Example 2.16: Creating and Interpreting a Stem-and-Leaf Plot (cont.) a. What were the smallest and largest salaries recorded? b. Which salary appears the most often? c. How many salaries were in the range \$41,000– \$41,900? d. In which salary range did the most salaries lie: \$40,000–\$44,900, \$45,000–\$49,900, or \$50,000 and above?

37. Example 2.16: Creating and Interpreting a Stem-and-Leaf Plot (cont.) Solution This example is different than Example 2.15 in that the data have more than two digits and every data value ends in two zeros. It is important to choose the stems of the numbers so that the last significant digit in each data point will be the leaf in the chart

38. Example 2.16: Creating and Interpreting a Stem-and-Leaf Plot (cont.) Listing the zeros as the leaves for each different stem would be of little use to anyone interpreting the stem-and-leaf plot. Instead, the key will denote that the salaries listed are in hundreds. Using the first two digits of each salary as a stem, we have the following plot. (Note: It is helpful to first list the salaries in numerical order.)

39. Example 2.16: Creating and Interpreting a Stem-and-Leaf Plot (cont.) a. The smallest salary is \$40,700; the largest salary is \$51,500. b. \$46,300 appears twice, which is more than any other salary. c. No salaries are in the range \$41,000–\$41,900 because there are no leaves listed for the stem 41. d. By counting the leaves in the given groups, we see that there are more salaries in the range \$45,000– \$49,900.

40. Dot Plots and Line Graphs A dot plot is similar to a stem-and-leaf plot because it retains the original data, however instead of grouping certain points together, the data that are exactly the same appear together. A line graph is used when data are measurements over time. The horizontal axis represents time. The vertical axis represents the variable being measured. Straight lines are used to connect points plotted at the value of each measurement above the time it was taken.

41. Example 2.17: Constructing a Line Graph The Consumer Price Index (CPI) is a measure of the average change in value over time for a basket of goods and services. It is an index calculated by the US Bureau of Labor and Statistics. The table below shows the values of the CPI from several years. Construct a line graph of these data.

42. Example 2.17: Constructing a Line Graph (cont.) Source: US Bureau of Labor Statistics. “Consumer Price Index History Table.” 19 Jan. 2012. ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt (24 Jan. 2012).

43. Summary Qualitative Data

44. Summary (cont.) Qualitative Data (cont.)