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Section 5.7 Describing the Spread: The Standard Deviation Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation The most common numerical description of a distribution is a combination of the mean with the standard deviation. The standard deviation and its close relative, the variance, measure spread by looking at how far the observations are from their mean. Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation Standard Deviation “s” is a kind of “standard” or average amount that the observed data values deviate from the mean. It is calculated by taking the square root of the mean of the squared deviations, except the mean is divided by (n – 1) instead of n. Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation The standard deviation of n observations x1,x2,x3 , ,xn is s x1 x 2 x2 x x3 x 2 2 n 1 xn x 2 Example 1 Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation Find the standard deviation of 7 purchase prices : 3 4 5 7 10 12 15 (in dollars) 1. Find the mean. 3 4 5 7 10 12 15 x 8 dollars 7 Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation 2. Find the standard deviation. 3 8 s 2 4 8 5 8 7 8 10 8 12 8 15 8 2 2 2 2 2 7 1 5 4 3 1 2 4 7 2 2 2 2 6 25 16 9 1 4 16 49 6 120 20 4.47 dollars 6 2 2 2 2 Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation 3. Find the variance. The variance is what is under the square root of the standard deviation. 120 120 var iance 20 7 1 6 Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation 1. 2. The variance is large if the observations are widely spread around the mean. The variance is small if the observations are all close to the mean. Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation Properties of the standard deviation “s”: 1. “s” measures spread about the mean. 2. s = 0 only when there is no spread, otherwise s > 0. 3. “s” has the same units of measurement as the original observations 4. “s” is sensitive to extreme observations or outliers Example 2 Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation Here are the systolic blood pressures of 10 randomly chosen adults: 147 141 120 124 127 132 98 112 120 128 Find the standard deviation of this data. Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation 1. Find the mean of the data. 147 141 120 124 127 132 98 112 120 128 x 10 1249 x 10 x 124.9 Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation 2. Subtract the mean from each observation. 147 – 124.9 = 22.1 132 – 124.9 = 7.1 141 – 124.9 = 16.1 98 – 124.9 = -26.9 120 – 124.9 = -4.9 112 – 124.9 = -12.9 124 – 124.9 = -0.9 120 – 124.9 = -4.9 127 – 124.9 = 2.1 128 – 124.9 = 3.1 Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation 3. Square the number that you found in #2. (22.1)2 = 488.41 (7.1)2 = 50.41 (16.1)2 = 259.21 (-26.9)2 = 723.61 (-4.9)2 = 24.01 (-12.9)2 = 166.41 (-0.9)2 = 0.81 (-4.9)2 = 24.01 (2.1)2 = 4.41 (3.1)2 = 9.61 Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation 4. Add the squared numbers and divide by (10 – 1). 488.41 259.21 24.01 0.81 4.41 50.41 723.61 166.41 24.01 9.61 10 1 1750.9 variance 9 variance 193.544 Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation 5. Take the square root of the variance. The answer will be the standard deviation of the data. s 194.544 s 13.948 Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation We now have a choice between two descriptions of the center and spread of a distribution: the five-number summary or the mean and standard deviation (s). Here is how to choose the correct summary. Chapter 5: Exploring Data: Distributions Describing Spread: The Standard Deviation Choosing a Summary 1. The five-number summary is usually better than the mean and standard deviation for describing a skewed distribution or a distribution with outliers. 2. Use the mean and standard deviation only for reasonably symmetric distributions with no outliers.