1. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Section Bias in Sampling 1.5

2. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 1-2 Objective 1. Explain the sources of bias in sampling

3. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 1-3 If the results of the sample are not representative of the population, then the sample has bias. Three Sources of Bias 1.Sampling Bias 2.Nonresponse Bias 3.Response Bias

4. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 1-4 Sampling bias means that the technique used to obtain the individuals to be in the sample tends to favor one part of the population over another. Undercoverage is a type of sampling bias. Undercoverage occurs when the proportion of one segment of the population is lower in a sample than it is in the population.

5. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 1-5 Nonresponse bias exists when individuals selected to be in the sample who do not respond to the survey have different opinions from those who do. Nonresponse can be improved through the use of callbacks or rewards/incentives.

6. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 1-6 Response bias exists when the answers on a survey do not reflect the true feelings of the respondent. Types of Response Bias 1.Interviewer error 2.Misrepresented answers 3.Words used in survey question 4.Order of the questions or words within the question

7. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 1-7 Nonsampling errors are errors that result from sampling bias, nonresponse bias, response bias, or data-entry error. Such errors could also be present in a complete census of the population. Sampling error is error that results from using a sample to estimate information about a population. This type of error occurs because a sample gives incomplete information about a population.

8. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Chapter Organizing and Summarizing Data 2

9. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Section Organizing Qualitative Data 2.1

10. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objectives 1.Organize Qualitative Data in Tables 2.Construct Bar Graphs 3.Construct Pie Charts

11. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-11 When data is collected from a survey or designed experiment, they must be organized into a manageable form. Data that is not organized is referred to as raw data. Ways to Organize Data Tables Graphs Numerical Summaries (Chapter 3)

12. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-12 Objective 1 Organize Qualitative Data in Tables

13. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-13 A frequency distribution lists each category of data and the number of occurrences for each category of data.

14. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-14 EXAMPLE Organizing Qualitative Data into a Frequency Distribution The data on the next slide represent the color of M&Ms in a bag of plain M&Ms. Construct a frequency distribution of the color of plain M&Ms.

15. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-15 EXAMPLE Organizing Qualitative Data into a Frequency Distribution brown, brown, yellow, red, red, red, brown, orange, blue, green, blue, brown, yellow, yellow, brown, red, red, brown, brown, brown, green, blue, green, orange, orange, yellow, yellow, yellow, red, brown, red, brown, orange, green, red, brown, yellow, orange, red, green, yellow, yellow, brown, yellow, orange

16. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-16 Frequency table ColorTallyFrequency Brown||||| ||||| ||12 Yellow||||| 10 Red||||| ||||9 Orange||||| |6 Blue|||3 Green|||||5

17. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-17 The relative frequency is the proportion (or percent) of observations within a category and is found using the formula: A relative frequency distribution lists each category of data with the relative frequency.

18. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-18 EXAMPLE Organizing Qualitative Data into a Relative Frequency Distribution Use the frequency distribution obtained in the prior example to construct a relative frequency distribution of the color of plain M&Ms.

19. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-19 Frequency table ColorTallyFrequency Brown||||| ||||| ||12 Yellow||||| 10 Red||||| ||||9 Orange||||| |6 Blue|||3 Green|||||5

20. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-20 ColorTallyFrequencyRelative Frequency Brown||||| ||||| ||1212/45 ≈ 0.2667 Yellow||||| 100.2222 Red||||| ||||90.2 Orange||||| |60.1333 Blue|||30.0667 Green|||||50.1111

21. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-21 Objective 2 Construct Bar Graphs

22. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-22 A bar graph is constructed by labeling each category of data on either the horizontal or vertical axis and the frequency or relative frequency of the category on the other axis. Rectangles of equal width are drawn for each category. The height of each rectangle represents the category’s frequency or relative frequency.

23. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-23 Use the M&M data to construct (a) a frequency bar graph and (b) a relative frequency bar graph. EXAMPLEConstructing a Frequency and Relative Frequency Bar Graph

24. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-24 Frequency table ColorTallyFrequency Brown||||| ||||| ||12 Yellow||||| 10 Red||||| ||||9 Orange||||| |6 Blue|||3 Green|||||5

25. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-25

26. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-26 ColorTallyFrequencyRelative Frequency Brown||||| ||||| ||1212/45 ≈ 0.2667 Yellow||||| 100.2222 Red||||| ||||90.2 Orange||||| |60.1333 Blue|||30.0667 Green|||||50.1111

27. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-27

28. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-28 EXAMPLEComparing Two Data Sets The following data represent the marital status (in millions) of U.S. residents 18 years of age or older in 1990 and 2006. Draw a side-by-side relative frequency bar graph of the data. Marital Status19902006 Never married40.455.3 Married112.6127.7 Widowed13.813.9 Divorced15.122.8

29. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-29 Relative Frequency Marital Status Marital Status in 1990 vs. 2006 1990 2006

30. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-30 Objective 3 Construct Pie Charts

31. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-31 A pie chart is a circle divided into sectors. Each sector represents a category of data. The area of each sector is proportional to the frequency of the category.

32. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-32 EXAMPLEConstructing a Pie Chart The following data represent the marital status (in millions) of U.S. residents 18 years of age or older in 2006. Draw a pie chart of the data. Marital StatusFrequency Never married55.3 Married127.7 Widowed13.9 Divorced22.8

33. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-33 EXAMPLEConstructing a Pie Chart

34. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Section Organizing Quantitative Data: The Popular Displays 2.2

35. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-35 Objectives 1.Organize discrete data in tables 2.Construct histograms of discrete data 3.Organize continuous data in tables 4.Construct histograms of continuous data 5.Draw stem-and-leaf plots 6.Identify the shape of a distribution

36. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-36 The first step in summarizing quantitative data is to determine whether the data are discrete or continuous. If the data are discrete and there are relatively few different values of the variable, the categories of data (classes) will be the observations (as in qualitative data). If the data are discrete, but there are many different values of the variables, or if the data are continuous, the categories of data (the classes) must be created using intervals of numbers.

37. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-37 Objective 1 Organize Discrete Data in Tables

38. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-38 The following data represent the number of available cars in a household based on a random sample of 50 households. Construct a frequency and relative frequency distribution. EXAMPLE Constructing Frequency and Relative Frequency Distribution from Discrete Data 3012111202422212202411324121223321220322232122113530121112024222122024113241212233212203222321221135 Data based on results reported by the United States Bureau of the Census.

39. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-39 # of Cars TallyFrequencyRelative Frequency 0||||44/50 = 0.08 1||||| ||||| |||1313/50 = 0.26 2||||| ||||| ||||| ||||| ||220.44 3||||| ||70.14 4|||30.06 5|10.02

40. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-40 Objective 2 Construct Histograms of Discrete Data

41. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-41 A histogram is constructed by drawing rectangles for each class of data. The height of each rectangle is the frequency or relative frequency of the class. The width of each rectangle is the same and the rectangles touch each other.

42. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-42 EXAMPLE Drawing a Histogram for Discrete Data Draw a frequency and relative frequency histogram for the “number of cars per household” data. # of CarsFrequencyRelative Frequency 044/50 = 0.08 11313/50 = 0.26 2220.44 370.14 430.06 510.02

43. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-43

44. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-44

45. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-45 Objective 3 Organize Continuous Data in Tables

46. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-46 Classes are categories into which data are grouped. When a data set consists of a large number of different discrete data values or when a data set consists of continuous data, we must create classes by using intervals of numbers.

47. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-47 The following data represents the number of persons aged 25 - 64 who are currently work-disabled. The lower class limit of a class is the smallest value within the class while the upper class limit of a class is the largest value within the class. The lower class limit of first class is 25. The lower class limit of the second class is 35. The upper class limit of the first class is 34. The class width is the difference between consecutive lower class limits. The class width of the data given above is 35 – 25 = 10.

48. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-48 EXAMPLEOrganizing Continuous Data into a Frequency and Relative Frequency Distribution The following data represent the time between eruptions (in seconds) for a random sample of 45 eruptions at the Old Faithful Geyser in Wyoming. Construct a frequency and relative frequency distribution of the data. Source: Ladonna Hansen, Park Curator

49. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-49 The smallest data value is 672 and the largest data value is 738. We will create the classes so that the lower class limit of the first class is 670 and the class width is 10 and obtain the following classes:

50. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-50 The smallest data value is 672 and the largest data value is 738. We will create the classes so that the lower class limit of the first class is 670 and the class width is 10 and obtain the following classes: 670 - 679 680 - 689 690 - 699 700 - 709 710 - 719 720 - 729 730 - 739

51. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-51 Time between Eruptions (seconds) TallyFrequenc y Relative Frequency 670 – 679||22/45 = 0.044 680 - 68900 690 - 699||||| ||70.1556 700 - 709||||| ||||90.2 710 - 719||||| ||||90.2 720 - 729||||| ||||| |110.2444 730 - 739||||| ||70.1556

52. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-52 The choices of the lower class limit of the first class and the class width were rather arbitrary. There is not one correct frequency distribution for a particular set of data. However, some frequency distributions can better illustrate patterns within the data than others. So constructing frequency distributions is somewhat of an art form. Use the distribution that seems to provide the best overall summary of the data.

53. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-53 Time between Eruptions (seconds) TallyFrequencyRelative Frequency 670 – 674|11/45 = 0.0222 675 - 679|10.0222 680 - 68400 685 - 68900 690 - 69400 695 - 699||||| ||70.1556 700 - 704||||| ||70.1556 705 - 709||20.0444 710 - 714|||||50.1111 715 - 719||||40.0889 720 - 724||||| |60.1333 725 - 729|||||50.1114 730 - 734|||30.0667 735 - 739||||40.0889

54. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-54 Guidelines for Determining the Lower Class Limit of the First Class and Class Width Choosing the Lower Class Limit of the First Class Choose the smallest observation in the data set or a convenient number slightly lower than the smallest observation in the data set.

55. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-55 Guidelines for Determining the Lower Class Limit of the First Class and Class Width Determining the Class Width Decide on the number of classes. Generally, there should be between 5 and 20 classes. The smaller the data set, the fewer classes you should have. Determine the class width by computing

56. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-56 Objective 4 Construct Histograms of Continuous Data

57. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-57 EXAMPLE Constructing a Frequency and Relative Frequency Histogram for Continuous Data Using class width of 10:

58. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-58

59. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-59 Using class width of 5:

60. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-60 Objective 5 Draw Stem-and-Leaf Plots

61. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-61 A stem-and-leaf plot uses digits to the left of the rightmost digit to form the stem. Each rightmost digit forms a leaf. For example, a data value of 147 would have 14 as the stem and 7 as the leaf.

62. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-62 EXAMPLEConstructing a Stem-and-Leaf Plot An individual is considered to be unemployed if they do not have a job, but are actively seeking employment. The following data represent the unemployment rate in each of the fifty United States plus the District of Columbia in June, 2008.

63. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-63 State13Unemployment RateStateUnemployment RateStateUnemployment Rate Alabama4.7Kentucky6.3North Dakota3.2 Alaska6.8Louisiana3.8Ohio6.6 Arizona4.8Maine5.3Oklahoma3.9 Arkansas5.0Maryland4.0Oregon5.5 California6.9Mass5.2Penn5.2 Colorado5.1Michigan8.5Rhode Island7.5 Conn5.4Minnesota5.3South Carolina6.2 Delaware4.2Mississippi6.9South Dakota2.8 Dist Col6.4Missouri5.7Tenn6.5 Florida5.5Montana4.1Texas4.4 Georgia5.7Nebraska3.3Utah3.2 Hawaii3.8Nevada6.4Vermont4.7 Idaho3.8New Hamp4.0Virginia4.0 Illinois6.8New Jersey5.3Washington5.5 Indiana5.8New Mexico3.9W. Virginia5.3 Iowa4.0New York5.3Wisconsin4.6 Kansas4.3North Carolina6.0Wyoming3.2

64. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-64 We let the stem represent the integer portion of the number and the leaf will be the decimal portion. For example, the stem of Alabama (4.7) will be 4 and the leaf will be 7.

65. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-65 2 8 3 888392922 4 782030104706 5 0145783237335253 6 89483940625 7 5 8 5

66. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-66 2 8 3 222388899 4 000012346778 5 0122333334555778 6 02344568899 7 5 8 5

67. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-67 Construction of a Stem-and-leaf Plot Step 1 The stem of a data value will consist of the digits to the left of the right- most digit. The leaf of a data value will be the rightmost digit. Step 2 Write the stems in a vertical column in increasing order. Draw a vertical line to the right of the stems. Step 3 Write each leaf corresponding to the stems to the right of the vertical line. Step 4 Within each stem, rearrange the leaves in ascending order, title the plot, and include a legend to indicate what the values represent.

68. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-68 When data appear rather bunched, we can use split stems. The stem-and-leaf plot shown on the next slide reveals the distribution of the data better. As with the determination of class intervals in the creation of frequency histograms, judgment plays a major role. There is no such thing as the correct stem-and-leaf plot. However, some plots are better than others.

69. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-69 A split stem-and-leaf plot: 2 8 3 2223 3 88899 4 00001234 4 6778 5 0122333334 5 555778 6 02344 6 568899 7 7 5 8 8 5 This stem represents 3.0 – 3.4 This stem represents 3.5 – 3.9

70. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-70 Once a frequency distribution or histogram of continuous data is created, the raw data is lost (unless reported with the frequency distribution), however, the raw data can be retrieved from the stem-and-leaf plot. Advantage of Stem-and-Leaf Diagrams over Histograms

71. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-71 Objective 7 Identify the Shape of a Distribution

72. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-72 Uniform distribution the frequency of each value of the variable is evenly spread out across the values of the variable Bell-shaped distribution the highest frequency occurs in the middle and frequencies tail off to the left and right of the middle Skewed right the tail to the right of the peak is longer than the tail to the left of the peak Skewed left the tail to the left of the peak is longer than the tail to the right of the peak.

73. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-73

74. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Section Additional Displays of Quantitative Data 2.3 Stat 212 – Introduction to Statistics Speaker: Mark Lesperance

75. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-75 Objectives 1.Draw time-series graphs

76. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-76 Objective 4 Draw Time Series Graphs

77. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-77 If the value of a variable is measured at different points in time, the data are referred to as time series data. A time-series plot is obtained by plotting the time in which a variable is measured on the horizontal axis and the corresponding value of the variable on the vertical axis. Line segments are then drawn connecting the points.

78. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-78 YearClosing Value 19902753.2 19912633.66 19923168.83 19933301.11 19943834.44 19955117.12 19966448.27 19977908.25 19989212.84 19999,181.43 200011,497.12 200110021.71 20028342.38 200310452.74 200410783.75 200510,783.01 200610,717.50 200713264.82 The data to the right shows the closing prices of the Dow Jones Industrial Average for the years 1990 – 2007.

79. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-79 Year Closing Value Dow Jones Industrial Average (1990 – 2007)

80. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Section Graphical Misrepresentations of Data 2.4 Stat 212 – Introduction to Statistics Speaker: Mark Lesperance

81. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-81 Objectives 1.Describe what can make a graph misleading or deceptive

82. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-82 Objective 1 Describe What Can Make a Graph Misleading or Deceptive

83. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-83 Statistics: The only science that enables different experts using the same figures to draw different conclusions. – Evan Esar

84. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-84 EXAMPLEMisrepresentation of Data The data in the table represent the historical life expectancies (in years) of residents of the United States. (a) Construct a misleading time series graph that implies that life expectancies have risen sharply. (b) Construct a time series graph that is not misleading. Year, xLife Expectancy, y 195068.2 196069.7 197070.8 198073.7 199075.4 200077.0 Source: National Center for Health Statistics

85. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-85 EXAMPLEMisrepresentation of Data (a) (b)

86. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-86 EXAMPLEMisrepresentation of Data The National Survey of Student Engagement is a survey that (among other things) asked first year students at liberal arts colleges how much time they spend preparing for class each week. The results from the 2007 survey are summarized on the next slide. (a) Construct a pie chart that exaggerates the percentage of students who spend between 6 and 10 hours preparing for class each week. (b) Construct a pie chart that is not misleading.

87. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-87 EXAMPLEMisrepresentation of Data HoursRelative Frequency 00 1 – 50.13 6 – 100.25 11 – 150.23 16 – 200.18 21 – 250.10 26 – 300.06 31 – 350.05 Source: http://nsse.iub.edu/NSSE_2007_Annual_Report/docs/withhold/NSSE_20 07_Annual_Report.pdf

88. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-88 (a)

89. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-89 (b)

90. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-90 Guidelines for Constructing Good Graphics Title and label the graphic axes clearly, providing explanations, if needed. Include units of measurement and a data source when appropriate. Avoid distortion. Never lie about the data. Minimize the amount of white space in the graph. Use the available space to let the data stand out. If scales are truncated, be sure to clearly indicate this to the reader.

91. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-91 Guidelines for Constructing Good Graphics Avoid clutter, such as excessive gridlines and unnecessary backgrounds or pictures. Don’t distract the reader. Avoid three dimensions. Three-dimensional charts may look nice, but they distract the reader and often lead to misinterpretation of the graphic.

92. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 2-92 Guidelines for Constructing Good Graphics Do not use more than one design in the same graphic. Sometimes graphs use a different design in one portion of the graph to draw attention to that area. Don’t try to force the reader to any specific part of the graph. Let the data speak for themselves. Avoid relative graphs that are devoid of data or scales.