Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Chapter Numerically Summarizing Data 3

Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Section Measures of Dispersion 3.2

Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 3-3 Objectives 1.Determine the range of a variable from raw data 2.Determine the standard deviation of a variable from raw data 3.Determine the variance of a variable from raw data 4.Use the Empirical Rule to describe data that are bell shaped 5.Use Chebyshev’s Inequality to describe any data set

Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 3-4 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?

Copyright © 2013, 2010 and 2007 Pearson Education, Inc Wait Time at Wendy’s Wait Time at McDonald’s

Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 3-6 (a) The mean wait time in each line is 1.39 minutes.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 3-7 (b)

Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 3-8 Objective 1 Determine the Range of a Variable from Raw Data

Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 3-9 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

Copyright © 2013, 2010 and 2007 Pearson Education, Inc EXAMPLEFinding the Range of a Set of Data The following data represent the travel times (in minutes) to work for all seven employees of a start-up web development company. 23, 36, 23, 18, 5, 26, 43 Find the range. Range = 43 – 5 = 38 minutes

Copyright © 2013, 2010 and 2007 Pearson Education, Inc Objective 2 Determine the Standard Deviation of a Variable from Raw Data

Copyright © 2013, 2010 and 2007 Pearson Education, Inc The population standard deviation of a variable is the square root of the sum of squared deviations about the population mean divided by the number of observations in the population, N. That is, it is the square root of the mean of the squared deviations about the population mean. The population standard deviation is symbolically represented by σ (lowercase Greek sigma).

Copyright © 2013, 2010 and 2007 Pearson Education, Inc where x 1, x 2,..., x N are the N observations in the population and μ is the population mean.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc A formula that is equivalent to the one on the previous slide, called the computational formula, for determining the population standard deviation is

Copyright © 2013, 2010 and 2007 Pearson Education, Inc EXAMPLE Computing a Population Standard Deviation The following data represent the travel times (in minutes) to work for all seven employees of a start- up web development company. 23, 36, 23, 18, 5, 26, 43 Compute the population standard deviation of this data.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc xixi μ x i – μ(x i – μ)

Copyright © 2013, 2010 and 2007 Pearson Education, Inc xixi (x i ) Σ x i = 174Σ (x i ) 2 = 5228 Using the computational formula, yields the same result.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc The sample standard deviation, s, of a variable is the square root of the sum of squared deviations about the sample mean divided by n – 1, where n is the sample size. where x 1, x 2,..., x n are the n observations in the sample and is the sample mean.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc A formula that is equivalent to the one on the previous slide, called the computational formula, for determining the sample standard deviation is

Copyright © 2013, 2010 and 2007 Pearson Education, Inc We call n - 1 the degrees of freedom because the first n - 1 observations have freedom to be whatever value they wish, but the n th value has no freedom. It must be whatever value forces the sum of the deviations about the mean to equal zero.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc EXAMPLEComputing a Sample Standard Deviation Here are the results of a random sample taken from the travel times (in minutes) to work for all seven employees of a start-up web development company: 5, 26, 36 Find the sample standard deviation.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc xixi

Copyright © 2013, 2010 and 2007 Pearson Education, Inc xixi (x i ) Σ x i = 67Σ (x i ) 2 = 1997 Using the computational formula, yields the same result.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 3-24

Copyright © 2013, 2010 and 2007 Pearson Education, Inc EXAMPLE Comparing Standard Deviations Determine the standard deviation waiting time for Wendy’s and McDonald’s. Which is larger? Why?

Copyright © 2013, 2010 and 2007 Pearson Education, Inc Wait Time at Wendy’s Wait Time at McDonald’s

Copyright © 2013, 2010 and 2007 Pearson Education, Inc EXAMPLE Comparing Standard Deviations Sample standard deviation for Wendy’s: minutes Sample standard deviation for McDonald’s: minutes Recall from earlier that the data is more dispersed for McDonald’s resulting in a larger standard deviation.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc Objective 3 Determine the Variance of a Variable from Raw Data

Copyright © 2013, 2010 and 2007 Pearson Education, Inc The variance of a variable is the square of the standard deviation. The population variance is σ 2 and the sample variance is s 2.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc EXAMPLE Computing a Population Variance The following data represent the travel times (in minutes) to work for all seven employees of a start-up web development company. 23, 36, 23, 18, 5, 26, 43 Compute the population and sample variance of this data.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc EXAMPLE Computing a Population Variance Recall that the population standard deviation (from slide #49) is σ = so the population variance is σ 2 = minutes and that the sample standard deviation (from slide #55) is s = 15.82, so the sample variance is s 2 = minutes

Copyright © 2013, 2010 and 2007 Pearson Education, Inc Objective 4 Use the Empirical Rule to Describe Data that are Bell Shaped

Copyright © 2013, 2010 and 2007 Pearson Education, Inc The Empirical Rule If a distribution is roughly bell shaped, then Approximately 68% of the data will lie within 1 standard deviation of the mean. That is, approximately 68% of the data lie between μ – 1σ and μ + 1σ. Approximately 95% of the data will lie within 2 standard deviations of the mean. That is, approximately 95% of the data lie between μ – 2σ and μ + 2σ.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc The Empirical Rule If a distribution is roughly bell shaped, then Approximately 99.7% of the data will lie within 3 standard deviations of the mean. That is, approximately 99.7% of the data lie between μ – 3σ and μ + 3σ. Note: We can also use the Empirical Rule based on sample data with used in place of μ and s used in place of σ.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 3-35

Copyright © 2013, 2010 and 2007 Pearson Education, Inc EXAMPLE Using the Empirical Rule The following data represent the serum HDL cholesterol of the 54 female patients of a family doctor

Copyright © 2013, 2010 and 2007 Pearson Education, Inc (a) Compute the population mean and standard deviation. (b) Draw a histogram to verify the data is bell-shaped. (c) Determine the percentage of all patients that have serum HDL within 3 standard deviations of the mean according to the Empirical Rule. (d) Determine the percentage of all patients that have serum HDL between 34 and 69.1 according to the Empirical Rule. (e) Determine the actual percentage of patients that have serum HDL between 34 and 69.1.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc (a) Using a TI-83 plus graphing calculator, we find (b)

Copyright © 2013, 2010 and 2007 Pearson Education, Inc (e) 45 out of the 54 or 83.3% of the patients have a serum HDL between 34.0 and (c) According to the Empirical Rule, 99.7% of the all patients that have serum HDL within 3 standard deviations of the mean. (d) 13.5% + 34% + 34% = 81.5% of all patients will have a serum HDL between 34.0 and 69.1 according to the Empirical Rule.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc Objective 5 Use Chebyshev’s Inequality to Describe Any Set of Data

Copyright © 2013, 2010 and 2007 Pearson Education, Inc Chebyshev’s Inequality For any data set or distribution, at least of the observations lie within k Note: We can also use Chebyshev’s Inequality based on sample data. standard deviations of the mean, where k is any number greater than 1. That is, at least of the data lie between μ – kσ and μ + kσ for k > 1.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc EXAMPLE Using Chebyshev’s Theorem Using the data from the previous example, use Chebyshev’s Theorem to (a) determine the percentage of patients that have serum HDL within 3 standard deviations of the mean. (b) determine the actual percentage of patients that have serum HDL between 34 and 80.8 (within 3 SD of mean). 52/54 ≈ 0.96 ≈ 96%