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

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Page 1: Chapter Numerically Summarizing Data © 2010 Pearson Prentice Hall. All rights reserved 3 3.

Chapter

Numerically Summarizing Data

© 2010 Pearson Prentice Hall. All rights reserved

33

Page 2: Chapter Numerically Summarizing Data © 2010 Pearson Prentice Hall. All rights reserved 3 3.

Section 3.1 Measures of Central Tendency

Objectives

1. Determine the arithmetic mean of a variable from raw data

2. Determine the median of a variable from raw data

3. Explain what it means for a statistics to be resistant

4. Determine the mode of a variable from raw data

3-2© 2010 Pearson Prentice Hall. All rights reserved

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

Objective 1

• Determine the arithmetic mean of a variable from raw data

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

Page 4: Chapter Numerically Summarizing Data © 2010 Pearson Prentice Hall. All rights reserved 3 3.

The arithmetic mean of a variable is computed by determining the sum of all the values of the variable in the data set divided by the number of observations.

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Page 5: Chapter Numerically Summarizing Data © 2010 Pearson Prentice Hall. All rights reserved 3 3.

The population arithmetic mean is computed using all the individuals in a population.

The population mean is a parameter.

The population arithmetic mean is denoted by .

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Page 6: Chapter Numerically Summarizing Data © 2010 Pearson Prentice Hall. All rights reserved 3 3.

The sample arithmetic mean is computed using sample data.

The sample mean is a statistic.

The sample arithmetic mean is denoted by .

x

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

Page 7: Chapter Numerically Summarizing Data © 2010 Pearson Prentice Hall. All rights reserved 3 3.

If x1, x2, …, xN are the N observations of a variable from a population, then the population mean, µ, is

1 2 Nx x x

N

3-7© 2010 Pearson Prentice Hall. All rights reserved

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If x1, x2, …, xn are the n observations of a variable from a sample, then the sample mean, , is

1 2 nx x xx

n

x

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

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EXAMPLE Computing a Population Mean and a Sample Mean

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

(a) Compute the population mean of this data.

(b)Then take a simple random sample of n = 3 employees. Compute the sample mean. Obtain a second simple random sample of n = 3 employees. Again compute the sample mean.

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

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EXAMPLE Computing a Population Mean and a Sample Mean

1 2 7...

7

ix

Nx x x

23 36 23 18 5 26 43

7

174

7

24.9 minutes

(a)

3-10© 2010 Pearson Prentice Hall. All rights reserved

Page 11: Chapter Numerically Summarizing Data © 2010 Pearson Prentice Hall. All rights reserved 3 3.

EXAMPLE Computing a Population Mean and a Sample Mean

(b) Obtain a simple random sample of size n = 3 from the population of seven employees. Use this simple random sample to determine a sample mean. Find a second simple random sample and determine the sample mean.

1 2 3 4 5 6 7

23, 36, 23, 18, 5, 26, 43

5 36 26

322.3

x

36 23 26

328.3

x

3-11© 2010 Pearson Prentice Hall. All rights reserved

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3-12© 2010 Pearson Prentice Hall. All rights reserved

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Objective 2

• Determine the median of a variable from raw data

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Page 14: Chapter Numerically Summarizing Data © 2010 Pearson Prentice Hall. All rights reserved 3 3.

The median of a variable is the value that lies in the middle of the data when arranged in ascending order. We use M to represent the median.

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EXAMPLE Computing a Median of a Data Set with an Odd Number of Observations

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

Determine the median of this data.

Step 1: 5, 18, 23, 23, 26, 36, 43

Step 2: There are n = 7 observations.

1 7 14

2 2

n Step 3: M = 23

5, 18, 23, 23, 26, 36, 43

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Page 17: Chapter Numerically Summarizing Data © 2010 Pearson Prentice Hall. All rights reserved 3 3.

EXAMPLE Computing a Median of a Data Set with an Even Number of Observations

Suppose the start-up company hires a new employee. The travel time of the new employee is 70 minutes. Determine the median of the “new” data set.

23, 36, 23, 18, 5, 26, 43, 70

Step 1: 5, 18, 23, 23, 26, 36, 43, 70

Step 2: There are n = 8 observations.

1 8 14.5

2 2

n Step 3:

5, 18, 23, 23, 26, 36, 43, 70

23 2624.5 minutes

2M

24.5M

3-17© 2010 Pearson Prentice Hall. All rights reserved

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Objective 3

• Explain what it means for a statistic to be resistant

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Page 19: Chapter Numerically Summarizing Data © 2010 Pearson Prentice Hall. All rights reserved 3 3.

EXAMPLE Computing a Median of a Data Set with an Even Number of Observations

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

Suppose a new employee is hired who has a 130 minute commute. How does this impact the value of the mean and median?

Mean before new hire: 24.9 minutesMedian before new hire: 23 minutes

Mean after new hire: 38 minutesMedian after new hire: 24.5 minutes

3-19© 2010 Pearson Prentice Hall. All rights reserved

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A numerical summary of data is said to be resistant if extreme values (very large or small) relative to the data do not affect its value substantially.

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EXAMPLE Describing the Shape of the Distribution

The following data represent the asking price of homes for sale in Lincoln, NE.

Source: http://www.homeseekers.com

79,995 128,950 149,900 189,900

99,899 130,950 151,350 203,950

105,200 131,800 154,900 217,500

111,000 132,300 159,900 260,000

120,000 134,950 163,300 284,900

121,700 135,500 165,000 299,900

125,950 138,500 174,850 309,900

126,900 147,500 180,000 349,900

3-22© 2010 Pearson Prentice Hall. All rights reserved

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Find the mean and median. Use the mean and median to identify the shape of the distribution. Verify your result by drawing a histogram of the data.

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Find the mean and median. Use the mean and median to identify the shape of the distribution. Verify your result by drawing a histogram of the data.

The mean asking price is $168,320 and the median asking price is $148,700. Therefore, we would conjecture that the distribution is skewed right.

3-24© 2010 Pearson Prentice Hall. All rights reserved

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350000300000250000200000150000100000

12

10

8

6

4

2

0

Asking Price

Frequency

Asking Price of Homes in Lincoln, NE

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

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Objective 4

• Determine the mode of a variable from raw data

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Page 27: Chapter Numerically Summarizing Data © 2010 Pearson Prentice Hall. All rights reserved 3 3.

The mode of a variable is the most frequent observation of the variable that occurs in the data set.

If there is no observation that occurs with the most frequency, we say the data has no mode.

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EXAMPLE Finding the Mode of a Data Set

The data on the next slide represent the Vice Presidents of the United States and their state of birth. Find the mode.

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The mode is New York.

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Tally data to determine most frequent observation

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