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Measures of Variation: Range, Deviation, Variance, and Standard Deviation

Learn how to calculate and interpret measures of variation, including range, deviation, variance, and standard deviation.

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Measures of Variation: Range, Deviation, Variance, and Standard Deviation

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  1. Chapter 2 Descriptive Statistics

  2. Chapter Outline • 2.1 Frequency Distributions and Their Graphs • 2.2 More Graphs and Displays • 2.3 Measures of Central Tendency • 2.4 Measures of Variation • 2.5 Measures of Position

  3. Section 2.4 Measures of Variation .

  4. Section 2.4 Objectives • How to find the range of a data set • How to find the variance and standard deviation of a population and of a sample • How to use the Empirical Rule and Chebychev’s Theorem to interpret standard deviation • How to approximate the sample standard deviation for grouped data • How to use the coefficient of variation to compare variation in different data sets .

  5. Range Range • The difference between the maximum and minimum data entries in the set. • The data must be quantitative. • Range = (Max. data entry) – (Min. data entry) .

  6. Example: Finding the Range A corporation hired 10 graduates. The starting salaries for each graduate are shown. Find the range of the starting salaries. Starting salaries (1000s of dollars) 41 38 39 45 47 41 44 41 37 42 .

  7. minimum maximum Solution: Finding the Range • Ordering the data helps to find the least and greatest salaries. 37 38 39 41 41 41 42 44 45 47 • Range = (Max. salary) – (Min. salary) = 47 – 37 = 10 The range of starting salaries is 10 or $10,000. .

  8. Deviation, Variance, and Standard Deviation Deviation • The difference between the data entry, x, and the mean of the data set. • Population data set: • Deviation of x = x – μ • Sample data set: • Deviation of x = x – x .

  9. Example: Finding the Deviation A corporation hired 10 graduates. The starting salaries for each graduate are shown. Find the deviation of the starting salaries. Starting salaries (1000s of dollars) 41 38 39 45 47 41 44 41 37 42 • Solution: • First determine the mean starting salary. .

  10. Determine the deviation for each data entry. Solution: Finding the Deviation Σ(x – μ) = 0 Σx = 415 .

  11. Deviation, Variance, and Standard Deviation Population Variance Population Standard Deviation Sum of squares, SSx .

  12. Finding the Population Variance & Standard Deviation In Words In Symbols • Find the mean of the population data set. • Find deviation of each entry. • Square each deviation. • Add to get the sum of squares. x – μ (x – μ)2 SSx = Σ(x – μ)2 .

  13. Finding the Population Variance & Standard Deviation In Words In Symbols • Divide by N to get the population variance. • Find the square root to get the population standard deviation. .

  14. Example: Finding the Population Standard Deviation A corporation hired 10 graduates. The starting salaries for each graduate are shown. Find the population variance and standard deviation of the starting salaries. Starting salaries (1000s of dollars) 41 38 39 45 47 41 44 41 37 42 Recall μ = 41.5. .

  15. Determine SSx N = 10 Solution: Finding the Population Standard Deviation Σ(x – μ) = 0 SSx = 88.5 .

  16. Solution: Finding the Population Standard Deviation Population Variance Population Standard Deviation The population standard deviation is about 3.0, or $3000. .

  17. Deviation, Variance, and Standard Deviation Sample Variance Sample Standard Deviation .

  18. Finding the Sample Variance & Standard Deviation In Words In Symbols • Find the mean of the sample data set. • Find deviation of each entry. • Square each deviation. • Add to get the sum of squares. .

  19. Finding the Sample Variance & Standard Deviation In Words In Symbols • Divide by n – 1 to get the sample variance. • Find the square root to get the sample standard deviation. .

  20. Example: Finding the Sample Standard Deviation The starting salaries are for the Chicago branches of a corporation. The corporation has several other branches, and you plan to use the starting salaries of the Chicago branches to estimate the starting salaries for the larger population. Find the sample standard deviation of the starting salaries. Starting salaries (1000s of dollars) 41 38 39 45 47 41 44 41 37 42 .

  21. Determine SSx n = 10 Solution: Finding the Sample Standard Deviation Σ(x – μ) = 0 SSx = 88.5 .

  22. Solution: Finding the Sample Standard Deviation Sample Variance Sample Standard Deviation The sample standard deviation is about 3.1, or $3100. .

  23. Example: Using Technology to Find the Standard Deviation Sample office rental rates (in dollars per square foot per year) for Miami’s central business district are shown in the table. Use a calculator or a computer to find the mean rental rate and the sample standard deviation. (Adapted from: Cushman & Wakefield Inc.) .

  24. Solution: Using Technology to Find the Standard Deviation Sample Mean Sample Standard Deviation .

  25. Interpreting Standard Deviation • Standard deviation is a measure of the typical amount an entry deviates from the mean. • The more the entries are spread out, the greater the standard deviation. .

  26. Interpreting Standard Deviation: Empirical Rule (68 – 95 – 99.7 Rule) For data with a (symmetric) bell-shaped distribution, the standard deviation has the following characteristics: • About 68%of the data lie within one standard deviation of the mean. • About95%of the data lie within two standard deviations of the mean. • About99.7%of the data lie within three standard deviations of the mean. .

  27. Interpreting Standard Deviation: Empirical Rule (68 – 95 – 99.7 Rule) 99.7% within 3 standard deviations 95% within 2 standard deviations 68% within 1 standard deviation 34% 34% 2.35% 2.35% 13.5% 13.5% .

  28. Example: Using the Empirical Rule In a survey conducted by the National Center for Health Statistics, the sample mean height of women in the United States (ages 20-29) was 64.3 inches, with a sample standard deviation of 2.62 inches. Estimate the percent of the women whose heights are between 59.06 inches and 64.3 inches. .

  29. Solution: Using the Empirical Rule • Because the distribution is bell-shaped, you can use the Empirical Rule. 34% + 13.5% = 47.5% of women are between 59.06 and 64.3 inches tall. .

  30. Chebychev’s Theorem • The portion of any data set lying within k standard deviations (k > 1) of the mean is at least: • k = 2: In any data set, at least of the data lie within 2 standard deviations of the mean. • k = 3: In any data set, at least of the data lie within 3 standard deviations of the mean. .

  31. Example: Using Chebychev’s Theorem The age distribution for Florida is shown in the histogram. Apply Chebychev’s Theorem to the data using k = 2. What can you conclude? .

  32. Solution: Using Chebychev’s Theorem k = 2: μ – 2σ = 39.2 – 2(24.8) = -10.4 (use 0 since age can’t be negative) μ + 2σ = 39.2 + 2(24.8) = 88.8 At least 75% of the population of Florida is between 0 and 88.8 years old. .

  33. Standard Deviation for Grouped Data Sample standard deviation for a frequency distribution • When a frequency distribution has classes, estimate the sample mean and standard deviation by using the midpoint of each class. where n= Σf (the number of entries in the data set) .

  34. You collect a random sample of the number of children per household in a region. Find the sample mean and the sample standard deviation of the data set. Example: Finding the Standard Deviation for Grouped Data .

  35. Solution: Finding the Standard Deviation for Grouped Data • First construct a frequency distribution. • Find the mean of the frequency distribution. The sample mean is about 1.8 children. Σf = 50 Σ(xf )= 91 .

  36. Solution: Finding the Standard Deviation for Grouped Data • Determine the sum of squares. .

  37. Solution: Finding the Standard Deviation for Grouped Data • Find the sample standard deviation. The standard deviation is about 1.7 children. .

  38. Coefficient of Variation Coefficient of Variation (CV) • Describes the standard deviation of a data set as a percent of the mean. • Population data set: • Sample data set: .

  39. The table shows the population heights (in inches) and weights (in pounds) of the members of a basketball team. Find the coefficient of variation for the heights and the weighs. Then compare the results. Example: Comparing Variation in Different Data Sets .

  40. The mean height is   72.8 inches with a standard deviation of   3.3 inches. The coefficient of variation for the heights is Solution: Comparing Variation in Different Data Sets .

  41. The mean weight is   187.8 pounds with a standard deviation of   17.7 pounds. The coefficient of variation for the weights is Solution: Comparing Variation in Different Data Sets The weights (9.4%) are more variable than the heights (4.5%). .

  42. Section 2.4 Summary • Found the range of a data set • Found the variance and standard deviation of a population and of a sample • Used the Empirical Rule and Chebychev’s Theorem to interpret standard deviation • Approximated the sample standard deviation for grouped data • Used the coefficient of variation to compare variation in different data sets .

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