#### Transcript Measures of Dispersion

Numerically Summarizing Data MM1D3 Measures of Dispersion To order food at a McDonald’s Restaurant, one must choose from multiple lines, while at Wendy’s Restaurant, one enters a single line. The following data represent the wait time (in minutes) in line for a simple random sample of 30 customers at each restaurant during the lunch hour. For each sample, answer the following: (a) What was the mean wait time? (b) Draw a histogram of each restaurant’s wait time. (c ) Which restaurant’s wait time appears more dispersed? Which line would you prefer to wait in? Why? Wait Time at Wendy’s 1.50 2.53 1.88 3.99 0.90 0.79 1.20 2.94 1.90 1.23 1.01 1.46 1.40 1.00 0.92 1.66 0.89 1.33 1.54 1.09 0.94 0.95 1.20 0.99 1.72 0.67 0.90 0.84 0.35 2.00 Wait Time at McDonald’s 3.50 0.00 1.97 0.00 3.08 0.00 0.26 0.71 0.28 2.75 0.38 0.14 2.22 0.44 0.36 0.43 0.60 4.54 1.38 3.10 1.82 2.33 0.80 0.92 2.19 3.04 2.54 0.50 1.17 0.23 The mean wait time in each line is 1.39 minutes. The range, R, of a variable is the difference between the largest data value and the smallest data values. That is Range = R = Largest Data Value – Smallest Data Value EXAMPLE Finding the Range of a Set of Data Find the range of the student data collected from Section 3.1 The population variance of a variable is the sum of squared deviations about the population mean divided by the number of observations in the population, N. That is it is the arithmetic mean of the sum of the squared deviations about the population mean. The population variance is symbolically represented by lower case Greek sigma squared. Note: When using the above formula, do not round until the last computation. Use as many decimals as allowed by your calculator in order to avoid round off errors. The sample variance is computed by determining the sum of squared deviations about the sample mean and then dividing this result by n – 1. Note: Whenever a statistic consistently overestimates or underestimates a parameter, it is called biased. To obtain an unbiased estimate of the population variance, we divide the sum of the squared deviations about the mean by n - 1. EXAMPLE Computing a Sample Variance Compute the sample variance using the sample data from Section 3.1 The population standard deviation is denoted by It is obtained by taking the square root of the population variance, so that EXAMPLE Comparing Standard Deviations Determine the standard deviation waiting time for Wendy’s and McDonald’s. Which is larger? Why? EXAMPLE Comparing Standard Deviations Determine the standard deviation waiting time for Wendy’s and McDonald’s. Which is larger? Why? Sample standard deviation for Wendy’s: 0.738 minutes Sample standard deviation for McDonald’s: 1.265 minutes EXAMPLE Using the Empirical Rule The following data represent the serum HDL cholesterol of the 54 female patients of a family doctor. 41 62 67 60 54 45 48 75 69 60 54 47 43 77 69 60 55 47 38 58 70 61 56 48 35 82 65 62 56 48 37 39 72 63 56 50 44 85 74 64 57 52 44 55 74 64 58 52 44 54 74 64 59 53 (a) Compute the population mean and standard deviation. (b) Draw a histogram to verify the data is bellshaped. (c) Determine the percentage of patients that have serum HDL within 3 standard deviations of the mean according to the Empirical Rule. (d) Determine the percentage of patients that have serum HDL between 34 and 80.8 according to the Empirical Rule. (e) Determine the actual percentage of patients that have serum HDL between 34 and 80.8. (a) Using a TI83 plus graphing calculator, we find 57.4 and 11.7 (b) 57.4 and 11.7 (c) According to the Empirical Rule, approximately 99.7% of the patients will have serum HDL cholesterol levels within 3 standard deviations of the mean. That is, approximately 99.7% of the patients will have serum HDL cholesterol levels greater than or equal to 57.4 - 3(11.7) = 22.3 and less than or equal to 57.4 + 3(11.7) = 92.5. 57.4 and 11.7 (d) Because 33.8 is 2 standard deviations below the mean (57.4 - 2(11.7) = 34) and 81 is 2 standard deviations above the mean (57.4 + 2(11.7) = 80.8), the Empirical Rule states that approximately 95% of the data will lie between 34 and 80.8. (e) There are no observations below 34. There are 2 observations greater than 80.8. Therefore, 52/54 = 96.3% of the data lie between 34 and 80.8.