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9.1 – Sampling Distributions

9.1 – Sampling Distributions. Many investigations and research projects try to draw conclusions about how the values of some variable x are distributed in a population. Often, attention is focused on a single characteristic of that distribution. Examples include:.

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9.1 – Sampling Distributions

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  1. 9.1 – Sampling Distributions

  2. Many investigations and research projects try to draw conclusions about how the values of some variable x are distributed in a population. Often, attention is focused on a single characteristic of that distribution. Examples include: 1. x = fat content (in grams) of a quarter-pound hamburger, with interest centered on the mean fat content μ of all such hamburgers

  3. 2. x = fuel efficiency (in miles per gallon) for a 2003 Honda Accord, with interest focused on the variability in fuel efficiency as described by σ, the standard deviation for the fuel efficiency population distribution 3. x = time to first recurrence of skin cancer for a patient treated using a particular therapy, with attention focused on p, the proportion of such individuals whose first recurrence is within 5 years of the treatment.

  4. Parameter: A number that describes the population. This number is typically unknown. Statistic: A number that describes the sample. We use this number to estimate the parameter.

  5. Parameter Statistic   p p

  6. Sampling Distribution: The distribution of all values taken by the statistic in all possible samples of the same size from the same population Ex: Take 100 samples of size n = 20.

  7. Sampling Variability: The variation between each groups of samples of the same size. If I compare many different samples and the statistic is very similar in each one, then the sampling variability is low. If I compare many different samples and the statistic is very different in each one, then the sampling variability is high.

  9. Unbiased: When the statistic is equal to the true value of the parameter Unbiased Estimator: The unbiased statistic

  10. How sampling works: • Take a large number of samples from the same population. 2. Calculate the sample mean or sample proportion for each sample 3. Make a histogram of the values of the statistics 4. Examine the distribution

  11. Facts about Samples: • If I chose a different sample, it would still represent the same population. A different sample almost always produces different statistics. • A statistic can be unbiased and still have high variablility. To avoid this, increase the size of the sample. Larger samples give smaller spread.

  12. Example #1: Classify each underlined number as a parameter or statistic. Give the appropriate notation for each. a. Forty-two percent of today’s 15-year-old girls will get pregnant in their teens. Parameter p = 0.42

  13. Example #1: Classify each underlined number as a parameter or statistic. Give the appropriate notation for each. b. The National Center for Health Statistics reports that the mean systolic blood pressure for males 35 to 44 years of age is 128 and the standard deviation is 15. The medical director of a large company looks at the medical records of 72 executives in this age group and finds that the mean systolic blood pressure for these executives is 126.07. 128 and 15 are parameters 126.07 is a statistic 128  = 126.07 15  =

  14. Example #2: Suppose you have a population in which 60% of the people approve of gambling. a. Is 60% a parameter or a statistic? Give appropriate notation for this value. p = 0.60 Parameter,

  15. You want to take many samples of size 10 from this population to observe how the sample proportion who approve of gambling vary in repeated samples. b. Describe the design of a simulation using the partial random digits table below to estimate the sample proportion who approve of gambling. Label how you will conduct the simulation. Then carry out five trials of your simulation. What is the average of the samples? How close is it to the 60%? Assign: 0 – 5 approve of gambling 6-9 don’t Stop: After choosing 10 Count: # of people that approve of gambling Repeaters: Ok to have repeat numbers, represent new person

  16. A D A A D A D A D A 3 6 0 0 9 1 9 3 6 5 1 5 4 1 2 3 9 6 3 8 8 5 4 5 3 4 6 8 1 6 3 8 4 4 8 4 8 7 8 9 1 8 3 3 8 2 4 6 9 7 3 9 3 6 4 4 2 0 0 6 8 2 7 3 9 5 7 8 9 0 2 0 8 0 7 4 7 5 1 1 8 1 6 7 6 5 5 3 0 0 6 0 9 4 0 7 2 0 2 4 1 7 8 6 8 2 4 9 4 3 6 1 7 9 0 9 0 6 5 6 6 8 4 1 7 3 5 0 1 3 1 5 5 2 9 7 2 7 6 5 8 5 0 8 9 5 7 0 6 7

  17. 1: 6/10 = 60% 2: 4/10 = 40% 3: 4/10 = 40% 4: 7/10 = 70% 5: 7/10 = 70%

  18. c. The sampling distribution of is the distribution of from all possible SRSs of size 10 from this population. What would be the mean of this distribution if this process was repeated 100 times? p = 0.60

  19. d. If you used samples of size 20 instead of size 10, which sampling distribution would give you a better estimate of the true proportion of people who approve of gambling? Explain your answer. 20, larger the sample size means less variability

  20. e. Make a histogram of the sample distribution. Describe the graph. C: 60% U: none S: Approx. symmetrical S: Range = 10-1 = 9

  21. 9.2 – Sample Proportions

  22. Using proportions:

  23. Remember Ch8? Use these when you know “p” What if you only know the proportion of a sample?

  24. Sampling Distribution of a Sample Proportion:

  25. Rule of Thumb #1: You can only use if the population is 10X the sample size . A census should be impractical! when N 10n

  26. Rule of Thumb #2: Only use the Normal approximation of the sampling distribution of when: and

  27. Conclusion: If p is the population proportion then, If is the sample proportion then, and ONLY if

  28. So, to calculate a Z-score for this! Or

  29. Example #1 Suppose you are going to roll a fair six-sided die 60 times and record , the proportion of times that a 1 or a 2 is showing. a. Where should the distribution of the 60 -values be centered?

  30. b. What is the standard deviation of the sampling distribution of , the proportion of all rolls of the die that show a 1 or a 2 out of the 60 rolls ? Rule of Thumb #1: Population is 10X sample size

  31. c. Describe the shape of the sampling distribution of Justify your answer. Rule of Thumb #2: and Approximately Normal.

  32. Example #2 According to government data, 22% of American children under the age of 6 live in households with incomes less than the official poverty level. A study of learning in early childhood chooses an SRS of 300 children. What is the probability that more than 20% of the sample are from poverty households? N 10n Rule of Thumb #1: N 10(300) N 3000 Population is 10X sample size, ok to use standard deviation

  33. Example #2 According to government data, 22% of American children under the age of 6 live in households with incomes less than the official poverty level. A study of learning in early childhood chooses an SRS of 300 children. What is the probability that more than 20% of the sample are from poverty households?

  34. Example #2 According to government data, 22% of American children under the age of 6 live in households with incomes less than the official poverty level. A study of learning in early childhood chooses an SRS of 300 children. What is the probability that more than 20% of the sample are from poverty households? Rule of Thumb #2: and Approximately Normal.

  35. Example #2 According to government data, 22% of American children under the age of 6 live in households with incomes less than the official poverty level. A study of learning in early childhood chooses an SRS of 300 children. What is the probability that more than 20% of the sample are from poverty households?

  36.  = 0.0239 0.20 0.22 P(Z – 0.8362) = 1 – P(Z – 0.8362)

  37.  = 0.0239 0.20 0.22 P(Z – 0.8362) = 1 – P(Z – 0.8362) = 1 – 0.2005 = 0.7995 Or: normalcdf(0.20, 1000000, 0.22, 0.0239) = 0.7985

  38. b. How large a sample would be needed to guarantee that the standard deviation of is no more than 0.01? Explain.

  39. 9.3 – Sample Means

  40. Sample Means Distribution:

  41. How do you determine normality? • If sample distribution is drawn from a Normal population, sample distribution is Normal, no matter how big n is • If sample distribution is drawn from a Skewed population, sample distribution is Skewed, if n is small.

  42. Central Limit Theorem: (CLT) • No matter what the population distribution looks like, if n 30, then the sample distribution is approximately normal.

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