1. Descriptive Statistics: Presenting and Describing Data

2. Frequency Distribution A table or graph describing the number of observations in each category or class of a data set.

3. Example: Consider the number of bottles of soda sold in a snack bar during lunch hour, on 40 days. (The numbers have been arranged in increasing order.) 6371768185 6673768285 6773768286 6874778486 6874788489 7075798490 7175798592 7175798594

4. In order to get a better grasp of this distribution of numbers, we’ll organize them into categories or classes. We’ll look at absolute frequency, relative frequency, cumulative absolute frequency, & cumulative relative frequency

5. Notation [20, 30] denotes all real numbers between 20 & 30, including the 20 & the 30. (20, 30) denotes all real numbers between 20 & 30, including neither the 20 nor the 30. [20, 30) denotes all real numbers between 20 & 30, including the 20 but not the 30. (20, 30] denotes all real numbers between 20 & 30, including the 30 but not the 20. So the square bracket means include that endpoint & the round parenthesis means do not include that endpoint.

6. Absolute Frequency classabs. freq. [60, 65) 1 [65, 70) 4 [70, 75) 8 [75, 80) 11 [80, 85) 6 [85, 90) 7 [90, 95) 3 40

7. Histogram or Bar Graph of Absolute Frequency 60 65 70 75 80 85 90 95 Bottles of soda Absolute frequency 12 10 8 6 4 2 0

8. classabs. freq.rel. freq. [60, 65) 10.025 [65, 70) 40.100 [70, 75) 80.200 [75, 80) 110.275 [80, 85) 60.150 [85, 90) 70.175 [90, 95) 30.075 401.000 Relative Frequency

9. 60 65 70 75 80 85 90 95 Bottles of soda Relative frequency 0.300 0.250 0.200 0.150 0.100 0.050 0.000 This graph looks the same as the last one, except the numbers on the vertical axis are percentages (in decimal form) instead of integers.

10. Frequency Polygon line connecting middle points of tops of bars 60 65 70 75 80 85 90 95 Bottles of soda Absolute frequency 12 10 8 6 4 2 0

11. Cumulative Absolute Frequency classabs. freq.rel. freq.cum. abs. freq. [60, 65) 10.025 1 [65, 70) 40.100 5 [70, 75) 80.200 13 [75, 80) 110.275 24 [80, 85) 60.150 30 [85, 90) 70.175 37 [90, 95) 30.075 40 401.000

12. Cumulative Absolute Frequency 60 65 70 75 80 85 90 95 Bottles of soda Cumulative Absolute Frequency 40 35 30 25 20 15 10 5 0 Notice that the graph of the cumulative absolute frequency looks like a set of stairs going up from left to right.

13. Cumulative Relative Frequency classabs. freq.rel. freq.cum. abs. freq.cum. rel. freq. [60, 65) 10.025 1 0.025 [65, 70) 40.100 5 0.125 [70, 75) 80.200 13 0.325 [75, 80) 110.275 24 0.600 [80, 85) 60.150 30 0.750 [85, 90) 70.175 37 0.925 [90, 95) 30.075 40 1.000 401.000

14. Cumulative Relative Frequency 60 65 70 75 80 85 90 95 Bottles of soda Cumulative Relative Frequency 1.00 0.75 0.50 0.25 0.00 Again we have our stairs, but the numbers on the vertical axis are percentages (in decimal form), and the height of the last bar is always 1 (or 100%).

15. Cumulative Relative Frequency Ogive 60 65 70 75 80 85 90 95 Bottles of soda Cumulative Relative Frequency 1.00 0.75 0.50 0.25 0.00 Line connecting the points at the back of the steps.

16. Next we will consider two types of summary measures: 1. Measures of the center of the distribution (also called measures of central tendency) 2. Measures of the spread of the distribution

17. Measures of the center of the distribution, or central tendency, or typical value, or average

18. Measures of the Center of the Distribution Mean or Arithmetic Mean: add up the values of the observations; then divide by the number of observations. Median: the value for which half of the observations are above that value & half are below it. Mode: Most common, most frequent, or most probable value.

19. Determining the location of the median Recall that the median is the value for which half of the observations are above that value & half are below it. So we are looking for the middle value. Suppose there are n numbers in our data set. We arrange them in order from the smallest value to the largest, and give the smallest value rank 1, the second smallest rank 2, and so forth up to the largest value, which has rank n. The rank of the median will be (n+1)/2.

20. Remember that n is the number of elements in the data set. We have two possible cases. Case 1: n is odd. Case 2: n is even.

21. Case 1: n is odd. Recall that the rank of the median is (n+1)/2. Example: n = 9 Then (n+1)/2 = (9+1)/2 = 10/5 = 5. So the value of the 5 th number is our median.

22. Case 2: n is even. Recall that the rank of the median is (n+1)/2. Example: n = 10 Then (n+1)/2 = (10+1)/2 = 11/2 = 5.5. So we are looking for the value halfway between the 5 th and 6 th numbers. So we add the values of the 5 th and 6 th numbers together and divide by 2. The result is our median.

23. Example 1 Observations2, 2, 3, 4, 8, 10, 13 Mean 6 Median4 Mode 2

24. Example 2 Observations-5, 8, 8, 9, 10, 12 Mean 7 Median 8.5 Mode 8

25. Example 3 Observations 2, 3, 4, 4, 4, 7 Mean 4 Median4 Mode 4

26. Example 4 Observations11, 9, 26, 11, 10, 11 To calculate the median, we will want to have the observations in order: 9, 10, 11, 11, 11, 26 Mean 13 Median11 Mode 11

27. Computing the Mean for a Frequency Distribution of a Population Salary x i Freq. f i 700 8 800 23 900 75 1000 90 1100 43 1200 11 250 We will denote the number of observations in our population as N. In this example, it’s 250.

28. Computing the Mean for a Frequency Distribution of a Population Salary x i Freq. f i 700 8 800 23 900 75 1000 90 1100 43 1200 11 250 First we need the sum of all the observations: (700 + 700 + 700 + … + 700) + (800 + 800 + 800 + … + 800) + … + (1200 + 1200 + 1200 + … + 1200)

29. Computing the Mean for a Frequency Distribution of a Population Salary x i Freq. f i 700 8 800 23 900 75 1000 90 1100 43 1200 11 250 First we need the sum of all the observations: (700 + 700 + 700 + … + 700) + (800 + 800 + 800 + … + 800) + … + (1200 + 1200 + 1200 + … + 1200) = (700 8) + (800 23) + (900 75) + (1000 90) + (1100 43) + (1200 11)

30. Computing the Mean for a Frequency Distribution of a Population Salary x i Freq. f i x i f i 700 8 5600 800 2318,400 900 7567,500 1000 9090,000 1100 4347,300 1200 1113,200 250 242,000

31. Computing the Mean for a Frequency Distribution of a Population Salary x i Freq. f i x i f i 700 8 5600 800 2318,400 900 7567,500 1000 9090,000 1100 4347,300 1200 1113,200 250 242,000 Then to get the mean, we will divide that sum by the number of observations.

32. Computing the Mean for a Frequency Distribution of a Population Salary x i Freq. f i x i f i 700 8 5600 800 2318,400 900 7567,500 1000 9090,000 1100 4347,300 1200 1113,200 250 242,000 So the mean equals 242,000 / 250 = 968.0.

33. Notation We denote the mean of a population by the Greek letter mu:  For a simple list of numbers, we computed  as: If c is the number of categories or classes in our frequency distribution, then we computed  for a frequency distribution as:

34. What is the mode of this frequency distribution? Salary x i Freq. f i 700 8 800 23 900 75 1000 90 1100 43 1200 11 250 The mode is the most frequent or most common value, which in this example is 1000.

35. What is the median of this frequency distribution? Salary x i Freq. f i 700 8 800 23 900 75 1000 90 1100 43 1200 11 250 Remember, the median is the middle value, or the average of the two middle values, when there is an even number of observations, as there is here.

36. Where is the median? The middle is between the salaries in the 125 th and 126 th positions, where there are 125 values below and 125 above. So we need to determine what salaries are in the 125 th and 126 th positions. Salary value: x x x … x x x x … x x x Position: 1 2 3 … 124 125 126 127 … 248 249 250

37. What is the median of this frequency distribution? Salary x i Freq. f i 700 8 800 23 900 75 1000 90 1100 43 1200 11 250 In the $700 category, we have observations 1 through 8. In the $800 category, we have observations 9 through 31 (= 8+23). In the $900 category, we have observations 32 through 106 (= 8+23+75). In the $1000 category, we have observations 107 through 196 (= 8+23+75+90). So the 125 th & 126 th observations are in the $1000 category. Averaging the values of the two middle observations together, we get (1000+1000)/2 = 1000. So our median is 1000.

38. Calculating mean & median for interval data. Suppose we have the following population data. Intervalfrequency f [0, 15)10 [15, 30)10 [30, 45)5 [45, 60)5 [60, 75)5

39. We will compute the mean first. We have 35 observations. Intervalfrequency f [0, 15)10 [15, 30)10 [30, 45)5 [45, 60)5 [60, 75)5 35

40. We need a representative element from each interval. For that we’ll use the midpoint. Intervalfrequency fmidpoint x [0, 15)107.5 [15, 30)1022.5 [30, 45)537.5 [45, 60)552.5 [60, 75)567.5 35

41. Now we continue as we did before to calculate the mean for a frequency distribution. Intervalfrequency fmidpoint xxf [0, 15)107.575.0 [15, 30)1022.5225.0 [30, 45)537.5187.5 [45, 60)552.5262.5 [60, 75)567.5337.5 35

42. Add up. Intervalfrequency fmidpoint xxf [0, 15)107.575.0 [15, 30)1022.5225.0 [30, 45)537.5187.5 [45, 60)552.5262.5 [60, 75)567.5337.5 35 1087.5

43. Divide by the number of observations, and we have the mean. Intervalfrequency fmidpoint xxf [0, 15)107.575.0 [15, 30)1022.5225.0 [30, 45)537.5187.5 [45, 60)552.5262.5 [60, 75)567.5337.5 35 1087.5  = 1087.5/35 = 31.07

44. Now let’s calculate the median. Intervalfrequency f [0, 15)10 [15, 30)10 [30, 45)5 [45, 60)5 [60, 75)5 35

45. To calculate the median of interval data, we need to make an assumption. We know the number of observations in each interval, but not exactly what they are. We’re going to assume that the observations are evenly distributed in the intervals.

46. Intervalfrequency f [0, 15)10 [15, 30)10 [30, 45)5 [45, 60)5 [60, 75)5 35 First, we need to figure out in which category the median is. There are 35 observations, so the middle one is the 18 th one. (There are 17 observations below the 18 th and 17 above it.) The first 10 observations are in the first category. The 11 th to the 20 th observations are in the second category. So the median must be in the second category.

47. The formula for calculating the median for interval data looks quite different from what we did before. L md is the lower limit on the category containing the median. N is the population size.  f p is the sum of the frequencies of the categories preceding the category containing the median. f md is the frequency of the category containing the median. width is the width of the interval containing the median.

48. Intervalfrequency f [0, 15)10 [15, 30)10 [30, 45)5 [45, 60)5 [60, 75)5 35 L md is the lower limit on the category containing the median. 15 N is the population size. 35  f p is the sum of the frequencies of the categories preceding the category containing the median. 10 f md is the frequency of the category containing the median. 10 width is the width of the interval containing the median. 15 Let’s go through the parts of the formula, keeping in mind that the median is in the second category.

49. Now we just assemble the pieces. Intervalfrequency f [0, 15)10 [15, 30)10 [30, 45)5 [45, 60)5 [60, 75)5 35

50. What does this mean? Intervalfrequency f [0, 15)10 [15, 30)10 [30, 45)5 [45, 60)5 [60, 75)5 35 Remember that the median is the 18 observation. That means it’s the 8 th observation of 10 in the second category. So it is closer to the end of that interval than the beginning. What the formula is telling us is that the median is 0.75 or ¾ of the way through the distance of 15 units, in the interval starting at 15.

51. Measures of dispersion or the spread of the distribution

52. Measures of Dispersion Range Mean Absolute Deviation (MAD) Mean Squared Deviation (MSD) Coefficient of Variation (CV) As we shall see, the first three are measures of absolute dispersion, while the CV is a measure of relative dispersion.

53. range largest value minus smallest value

54. Example 1 Observations: 1 2 2 2 3 4 4 5 6 The range is 6 -1 = 5

55. Example 2 Observations: 1 1 1 1 1 1 1 1 6 The range is 6 -1 = 5 Intuitively, this distribution seems to be less spread out than the distribution in Example 1, but the range doesn’t capture that.

56. Mean Absolute Deviation (MAD) This formula is for the MAD for a simple list of numbers. We’ll do the MAD for a frequency distribution shortly.

57. Example: x 4 8 10 13 15 First we need the mean.

58. Example: x 4 8 10 13 15 50

59. Example: x 4 8 10 13 15 50  = 50/5 = 10

60. Example: x x-  4-6 8-2 100 133 155 50  = 50/5 = 10

61. Example: x x-  | x-  | 4-6 6 8-2 2 100 0 133 3 155 5 50  = 50/5 = 10

62. Example: x x-  | x-  | 4-6 6 8-2 2 100 0 133 3 155 5 5016  = 50/5 = 10

63. Example: x x-  | x-  | 4-6 6 8-2 2 100 0 133 3 155 5 5016  = 50/5 = 10 MAD = 16/5 = 3.2

64. Population Variance or Mean Squared Deviation (MSD)

65. Example: x x-  4-6 8-2 100 133 155 Recall  = 10

66. Example: x x-  ( x-  ) 2 4-6 36 8-2 4 100 0 133 9 155 25 Recall  = 10

67. Example: x x-  ( x-  ) 2 4-6 36 8-2 4 100 0 133 9 155 25 74 Recall  = 10

68. Example: x x-  ( x-  ) 2 4-6 36 8-2 4 100 0 133 9 155 25 74 Recall  = 10   = MSD = 74/5 = 14.8

69. population standard deviation √Population Variance

70. Example: population standard deviation √14.8 = 3.847 In the example we just did, the population variance was 14.8. So the standard deviation is

71. Calculating the MAD, MSD, & Std. Dev. for a Frequency Distribution xixi fifi 13 25 32

72. The total number of observations N is the sum of the frequencies or 10. xixi fifi 13 25 32 10

73. Calculate the population mean  xixi fifi xifixifi 133 2510 326

74. Calculate the population mean  xixi fifi xifixifi 133 2510 326 19

75. Calculate the population mean  xixi fifi xifixifi 133 2510 326 19  = 19/10 =1.9

76. Calculate the Mean Absolute Deviation (MAD). xixi fifi xifixifi x i -  133 -0.9 2510 0.1 326 1.1 1019  = 19/10 =1.9

77. Calculate the Mean Absolute Deviation (MAD). xixi fifi xifixifi x i -  |x i –  | 133 -0.9 0.9 2510 0.1 326 1.1 1019  = 19/10 =1.9

78. Calculate the Mean Absolute Deviation (MAD). xixi fifi xifixifi x i -  |x i –  ||x i –  |f i 133 -0.9 0.92.7 2510 0.1 0.5 326 1.1 2.2 1019  = 19/10 =1.9

79. Calculate the Mean Absolute Deviation (MAD). xixi fifi xifixifi x i -  |x i –  ||x i –  |f i 133 -0.9 0.92.7 2510 0.1 0.5 326 1.1 2.2 1019  = 19/10 =1.9 5.4

80. Calculate the Mean Absolute Deviation (MAD). xixi fifi xifixifi x i -  |x i –  ||x i –  |f i 133 -0.9 0.92.7 2510 0.1 0.5 326 1.1 2.2 1019  = 19/10 =1.9 5.4 MAD = 5.4/10 = 0.54

81. Calculate the Mean Squared Deviation (MSD) or Population Variance  2. xixi fifi xifixifi x i -  |x i –  ||x i –  |f i (x i –   133 -0.9 0.92.70.81 2510 0.1 0.50.01 326 1.1 2.21.21 1019  = 19/10 =1.9 5.4 MAD = 5.4/10 = 0.54

82. Calculate the Mean Squared Deviation (MSD) or Population Variance  2. xixi fifi xifixifi x i -  |x i –  ||x i –  |f i (x i –   (x i –   f i 133 -0.9 0.92.70.812.43 2510 0.1 0.50.010.05 326 1.1 2.21.212.42 1019  = 19/10 =1.9 5.4 MAD = 5.4/10 = 0.54

83. Calculate the Mean Squared Deviation (MSD) or Population Variance  2. xixi fifi xifixifi x i -  |x i –  ||x i –  |f i (x i –   (x i –   f i 133 -0.9 0.92.70.812.43 2510 0.1 0.50.010.05 326 1.1 2.21.212.42 1019  = 19/10 =1.9 5.4 MAD = 5.4/10 = 0.54 4.90

84. Calculate the Mean Squared Deviation (MSD) or Population Variance  2. xixi fifi xifixifi x i -  |x i –  ||x i –  |f i (x i –   (x i –   f i 133 -0.9 0.92.70.812.43 2510 0.1 0.50.010.05 326 1.1 2.21.212.42 1019  = 19/10 =1.9 5.4 MAD = 5.4/10 = 0.54 4.90   = MSD = 4.90/10 = 0.49

85. Last, we calculate the standard deviation. xixi fifi xifixifi x i -  |x i –  ||x i –  |f i (x i –   (x i –   f i 133 -0.9 0.92.70.812.43 2510 0.1 0.50.010.05 326 1.1 2.21.212.42 1019  = 19/10 =1.9 5.4 MAD = 5.4/10 = 0.54 4.90   = MSD = 4.90/10 = 0.49

86. Last, we calculate the standard deviation. xixi fifi xifixifi x i -  |x i –  ||x i –  |f i (x i –   (x i –   f i 133 -0.9 0.92.70.812.43 2510 0.1 0.50.010.05 326 1.1 2.21.212.42 1019  = 19/10 =1.9 5.4 MAD = 5.4/10 = 0.54 4.90   = MSD = 4.90/10 = 0.49  =√0.49 = 0.7

87. So the formulae for calculating the MAD, and MSD (or population variance) are The standard deviation is still just the square root of the variance.

88. The formulae we have been using are for populations. If we have samples instead, we have some notational changes and one change in the calculation process.

89. Notational Changes for Samples instead of Populations First, the sample size is n instead of N. To calculate the sample mean for a simple list of numbers we have: Next, we denote the sample mean by “Xbar” instead of . To calculate the sample mean for a frequency distribution we have:

90. Mean Absolute Deviation (MAD) for a sample MAD for a simple list of numbers: MAD for a frequency distribution:

91. Sample Variance The MSD is just for the population variance. We don’t have an MSD for the sample. The calculation is also slightly different for the sample variance than it was for the population variance.

92. Sample Variance (denoted by s 2 ) Sample Variance for a simple list of numbers: The only change that is not just notational is that instead of dividing by n, we divide by n-1. The reason for this change is so that the sample variance will be an unbiased estimator of the population variance. We’ll discuss the idea of unbiasedness later in the semester. Sample Variance for a frequency distribution:

93. Coefficient of Variation (CV) Our other measures of the spread of the distribution were measures of absolute dispersion. The CV is calculated relative to the mean, so it is considered a relative measure of dispersion.

94. Coefficient of Variation (CV) The CV is simply the standard deviation divided by the mean multiplied by 100 to put it in percentage terms.

95. Example: Suppose you have data for a sample that has a mean of 200 and a standard deviation of 10. What is the coefficient of variation (CV)? This result tells us that the standard deviation is 5% as large as the mean.

96. At my web page, there is a summary sheet called “Selected Descriptive Statistics.” It shows some of the different formulae for simple lists of numbers and frequency distributions, for populations and samples. Print it out and look at how the formulae are similar and how they’re different.

97. Empirical Rule In most data sets, many of the values tend to cluster near the center of the distribution. In symmetric, bell-shaped distributions, approximately 68% of the values are within 1 standard deviations of the mean; approximately 95% of the values are within 2 standard deviations of the mean; and approximately 99.7% of the values are within 3 standard deviations of the mean.

98. Thus, values that are more than 3 standard deviations from the mean are very atypical and are often called “outliers.” Determining whether an observation is an outlier is equivalent to determining if its “z-score” is less than -3 or greater than +3. The formula for the z-score is:

99. Example Suppose that a particular sample has a mean of 100, and a standard deviation of 10. Would a value of 120 be considered an outlier? Since the Z-score is not less than -3 or greater than +3, 120 is not an outlier in this sample.

100. Example Is 150 an outlier in this sample (with mean 100 and standard deviation 10)? Since the Z-score is less than -3 or greater than +3, 150 is an outlier in this sample.

101. Example Is 60 an outlier in this sample (with mean 100 and standard deviation 10)? Since the Z-score is less than -3 or greater than +3, 60 is an outlier in this sample.

102. Symmetric versus Skewed Distributions

103. Symmetric Distribution For a symmetric distribution, the left and right sides are mirror images of each other. The mean, median, and mode are the same.

104. Positively or Right-Skewed Distribution If the longer tail is to the right, the distribution is positively skewed or skewed to the right. A small number of very large values pulls the mean up, so the mean is larger than the median.

105. Negatively or Left-Skewed Distribution If the longer tail is to the left, the distribution is negatively skewed or skewed to the left. A small number of very small values pulls the mean down, so the mean is smaller than the median.