Chapter Numerically Summarizing Data © 2010 Pearson Prentice Hall. All rights reserved 3 3

Section 3.2 Measures of Dispersion Objectives 1.Compute the range of a variable from raw data 2.Compute the variance of a variable from raw data 3.Compute the standard deviation 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 3-2© 2010 Pearson Prentice Hall. All rights reserved

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? 3-3© 2010 Pearson Prentice Hall. All rights reserved

Wait Time at Wendy’s Wait Time at McDonald’s 3-4© 2010 Pearson Prentice Hall. All rights reserved

(a) The mean wait time in each line is 1.39 minutes. 3-5© 2010 Pearson Prentice Hall. All rights reserved

(b) 3-6© 2010 Pearson Prentice Hall. All rights reserved

Objective 1 Compute the range of a variable from raw data 3-7© 2010 Pearson Prentice Hall. All rights reserved

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 3-8© 2010 Pearson Prentice Hall. All rights reserved

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 3-9© 2010 Pearson Prentice Hall. All rights reserved

Objective 2 Compute the variance of a variable from raw data 3-10© 2010 Pearson Prentice Hall. All rights reserved

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 mean of the sum of the squared deviations about the population mean. 3-11© 2010 Pearson Prentice Hall. All rights reserved

The population variance is symbolically represented by σ 2 (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. 3-12© 2010 Pearson Prentice Hall. All rights reserved

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 variance of this data. Recall that 3-13© 2010 Pearson Prentice Hall. All rights reserved

xixi μ x i – μ(x i – μ) minutes © 2010 Pearson Prentice Hall. All rights reserved

The Computational Formula 3-15© 2010 Pearson Prentice Hall. All rights reserved

EXAMPLE Computing a Population Variance Using the Computational Formula 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 variance of this data using the computational formula. 3-16© 2010 Pearson Prentice Hall. All rights reserved

23, 36, 23, 18, 5, 26, © 2010 Pearson Prentice Hall. All rights reserved

The sample variance is computed by determining the sum of squared deviations about the sample mean and then dividing this result by n – © 2010 Pearson Prentice Hall. All rights reserved

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 © 2010 Pearson Prentice Hall. All rights reserved

EXAMPLE Computing a Sample Variance In Section 3.1, we obtained the following simple random sample for the travel time data: 5, 36, 26. Compute the sample variance travel time. Travel Time, x i Sample Mean,Deviation about the Mean, Squared Deviations about the Mean, – = ( ) 2 = square minutes 3-20© 2010 Pearson Prentice Hall. All rights reserved

Objective 3 Compute the standard deviation of a variable from raw data 3-21© 2010 Pearson Prentice Hall. All rights reserved

The population standard deviation is denoted by It is obtained by taking the square root of the population variance, so that The sample standard deviation is denoted by s It is obtained by taking the square root of the sample variance, so that 3-22© 2010 Pearson Prentice Hall. All rights reserved

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. Recall, from the last objective that σ 2 = minutes 2. Therefore, 3-23© 2010 Pearson Prentice Hall. All rights reserved

EXAMPLEComputing a Sample Standard Deviation Recall the sample data 5, 26, 36 results in a sample variance of square minutes Use this result to determine the sample standard deviation. 3-24© 2010 Pearson Prentice Hall. All rights reserved

3-25© 2010 Pearson Prentice Hall. All rights reserved

EXAMPLE Comparing Standard Deviations Determine the standard deviation waiting time for Wendy’s and McDonald’s. Which is larger? Why? 3-26© 2010 Pearson Prentice Hall. All rights reserved

Wait Time at Wendy’s Wait Time at McDonald’s 3-27© 2010 Pearson Prentice Hall. All rights reserved

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: minutes Sample standard deviation for McDonald’s: minutes 3-28© 2010 Pearson Prentice Hall. All rights reserved

Objective 4 Use the Empirical Rule to Describe Data That Are Bell Shaped 3-29© 2010 Pearson Prentice Hall. All rights reserved

3-30© 2010 Pearson Prentice Hall. All rights reserved

3-31© 2010 Pearson Prentice Hall. All rights reserved

EXAMPLE Using the Empirical Rule The following data represent the serum HDL cholesterol of the 54 female patients of a family doctor © 2010 Pearson Prentice Hall. All rights reserved

(a) Compute the population mean and standard deviation. (b) Draw a histogram to verify the data is bell-shaped. (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 69.1 according to the Empirical Rule. (e) Determine the actual percentage of patients that have serum HDL between 34 and © 2010 Pearson Prentice Hall. All rights reserved

(a) Using a TI-83 plus graphing calculator, we find (b) 3-34© 2010 Pearson Prentice Hall. All rights reserved

(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 patients that have serum HDL within 3 standard deviations of the mean. (d) 13.5% + 34% + 34% = 81.5% of patients will have a serum HDL between 34.0 and 69.1 according to the Empirical Rule. 3-35© 2010 Pearson Prentice Hall. All rights reserved

Objective 5 Use Chebyshev’s Inequality to Describe Any Set of Data 3-36© 2010 Pearson Prentice Hall. All rights reserved

3-37© 2010 Pearson Prentice Hall. All rights reserved

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 © 2010 Pearson Prentice Hall. All rights reserved