1. SAMPLING DISTRIBUTIONS
CHAPTER 7 (Part C)
SAMPLING DISTRIBUTIONS
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2. 7.5 POPULATION AND SAMPLE PROPORTIONS
The population and sample proportions, denoted by p and , respectively, are calculated as
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3. POPULATION AND SAMPLE PROPORTIONS
where
N = total number of elements in the population
n = total number of elements in the sample
X = number of elements in the population that possess a specific characteristic
x = number of elements in the sample that possess a specific characteristic
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4. Example
Suppose a total of 789,654 families live in a city and 563,282 of them own homes. A sample of 240 families is selected from this city, and 158 of them own homes. Find the proportion of families who own homes in the population and in the sample.
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5. Example: Solution
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6. MEAN, STANDARD DEVIATION, AND SHAPE OF THE SAMPLING DISTRIBUTION OF
Mean and Standard Deviation of
Shape of the Sampling Distribution of
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7. Sampling Distribution of the Sample Proportion
Definition
The probability distribution of the sample proportion, , is called its sampling distribution. It gives various values that can assume and their probabilities.
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8. Example
Boe Consultant Associates has five employees. Table 7.6 gives the names of these five employees and information concerning their knowledge of statistics.
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9. Table 7.6 - Information on the Five Employees of Boe Consultant Associates
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10. Example
If we define the population proportion, p, as the proportion of employees who know statistics, then
p = 3 / 5 = .60
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11. Example
Now, suppose we draw all possible samples of three employees each and compute the proportion of employees, for each sample, who know statistics.
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12. Table - All Possible Samples of Size 3 and the Value of for Each Sample
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13. Table - Frequency and Relative Frequency Distribution of When the Sample Size Is 3
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14. Table - Sampling Distribution of When the Sample Size is 3
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15. Mean and Standard Deviation of
Mean of the Sample Proportion
The mean of the sample proportion, , is denoted by and is equal to the population proportion, p. Thus,
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16. Mean and Standard Deviation of
Standard Deviation of the Sample Proportion
The standard deviation of the sample proportion, , is denoted by and is given by the formula
where p is the population proportion, q = 1 – p , and n is the sample size. This formula is used when n/N ≤ .05, where N is the population size.
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17. Mean and Standard Deviation of
If n /N > .05, then is calculated as:
where the factor is called the finite- population correction factor.
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18. Shape of the Sampling Distribution of
Central Limit Theorem for Sample Proportion
According to the central limit theorem, the sampling distribution of is approximately normal for a sufficiently large sample size. In the case of proportion, the sample size is considered to be sufficiently large if np and nq are both greater than 5 – that is, if
np > 5 and nq >5
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19. Example
According to a survey by Harris Interactive conducted in February 2009 for the charitable agency World Vision, 56% of U.S. teens volunteer time for charitable causes. Assume that this result is true for the current population of all U.S. teens. Let be the proportion of U.S. teens in a random sample of 1500 who volunteer time for charitable causes. Find the mean and standard deviation of and describe the shape of its sampling distribution.
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20. Example: Solution
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21. Example: Solution np and nq are both greater than 5.
Therefore, the sampling distribution of
is approximately normal (by the central limit theorem) with a mean of .56 and a standard deviation of .0128, as shown in Figure
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22. Figure
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23. 7.6 Applications of the Sampling Distribution of
Example
According to the BBMG Conscious Consumer Report, 51% of the adults surveyed said that they are willing to pay more for products with social and environmental benefits despite the current tough economic times (USA TODAY, June 8, 2009). Suppose that this result is true for the current population of adult Americans. Let be the proportion in a random sample of 1050 adult Americans who will hold the said opinion. Find the probability that the value of is between
.53 and .55.
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24. Example: Solution n =1050, p = .51, and q = 1 – p = 1 - .51 = .49
We can infer from the central limit theorem that the sampling distribution of is approximately normal.
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25. Figure
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26. z Value for a Value of The z value for a value of is calculated as
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27. Example: Solution For = .53: For = .55:
P(.53 < < .55) = P(1.30 < z < 2.59) =
= .0920
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28. Example: Solution
Thus, the probability is that the proportion of U.S. adults in a random sample of 1050 who will be willing to pay more for products with social and environmental benefits despite the current tough economic times is between .53 and .55.
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29. Figure
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30. Example
Maureen Webster, who is running for mayor in a large city, claims that she is favored by 53% of all eligible voters of that city. Assume that this claim is true. What is the probability that in a random sample of 400 registered voters taken from this city, less than 49% will favor Maureen Webster?
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31. Example: Solution n =400, p = .53, and q = 1 – p = 1 - .53 = .47
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32. Example: Solution P( < .49) = P(z < -1.60) = .0548
Hence, the probability that less than 49% of the voters in a random sample of 400 will favor Maureen Webster is
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33. Figure
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