Chapter 18. Sampling Distribution Models. Copyright 2010, 2007, 2004 Pearson Education, Inc.


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2 Chapter 18 Sampling Distribution Models Copyright 2010, 2007, 2004 Pearson Education, Inc.
3 Normal Model When we talk about one data value and the Normal model we used the notation: N(μ, σ) Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 183
4 Scenario Suppose everyone in AP Stats at Geneva High School took a survey which entailed the question Do you believe in ghosts? Are the answers received going to be categorical or quantitative? Would everyone get the same proportion of students that said yes and no? Why or why not? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 184
5 Categorical Data Deals with sample proportions Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 185
6 The Central Limit Theorem for Sample Proportions Rather than showing real repeated samples, imagine what would happen if we were to actually draw many samples. Now imagine what would happen if we looked at the sample proportions for these samples. The histogram we d get if we could see all the proportions from all possible samples is called the sampling distribution of the proportions. What would the histogram of all the sample proportions look like? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 186
7 Modeling the Distribution of Sample Proportions (cont.) We would expect the histogram of the sample proportions to center at the true proportion, p, in the population. As far as the shape of the histogram goes, we can simulate a bunch of random samples that we didn t really draw. It turns out that the histogram is unimodal, symmetric, and centered at p. More specifically, it s an amazing and fortunate fact that a Normal model is just the right one for the histogram of sample proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 187
8 Modeling the Distribution of Sample Proportions (cont.) Modeling how sample proportions vary from sample to sample is one of the most powerful ideas we ll see in this course. A sampling distribution model for how a sample proportion varies from sample to sample allows us to quantify that variation and how likely it is that we d observe a sample proportion in any particular interval. To use a Normal model, we need to specify its mean and standard deviation. We ll put µ, the mean of the Normal, at p. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 188
9 Modeling the Distribution of Sample Proportions (cont.) When working with proportions, knowing the mean automatically gives us the standard deviation as well the standard deviation we will use is So, the distribution of the sample proportions is modeled with a probability model that is N p, pq n pq n Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 189
10 Modeling the Distribution of Sample Proportions (cont.) A picture of what we just discussed is as follows: Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1810
11 The Central Limit Theorem for Sample Proportions (cont) Because we have a Normal model, for example, we know that 95% of Normally distributed values are within two standard deviations of the mean. So we should not be surprised if 95% of various polls gave results that were near the mean but varied above and below that by no more than two standard deviations. This is what we mean by sampling error. It s not really an error at all, but just variability you d expect to see from one sample to another. A better term would be sampling variability. A reasonable variance/error is ±2σ Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1811
12 How Good Is the Normal Model? The Normal model gets better as a good model for the distribution of sample proportions as the sample size gets bigger. Just how big of a sample do we need? This will soon be revealed Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1812
13 Assumptions and Conditions Most models are useful only when specific assumptions are true. There are two assumptions in the case of the model for the distribution of sample proportions: 1. The Independence Assumption: The sampled values must be independent of each other. 2. The Sample Size Assumption: The sample size, n, must be large enough. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1813
14 Assumptions and Conditions (cont.) Assumptions are hard often impossible to check. That s why we assume them. Still, we need to check whether the assumptions are reasonable by checking conditions that provide information about the assumptions. The corresponding conditions to check before using the Normal to model the distribution of sample proportions are the Randomization Condition, the 10% Condition and the Success/Failure Condition. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1814
15 Assumptions and Conditions (cont.) 1. Randomization Condition: The sample should be a simple random sample of the population % Condition: the sample size, n, must be no larger than 10% of the population. 3. Success/Failure Condition: The sample size has to be big enough so that both np (number of successes) and nq (number of failures) are at least 10. So, we need a large enough sample that is not too large. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1815
16 A Sampling Distribution Model for a Proportion A proportion is no longer just a computation from a set of data. It is now a random variable quantity that has a probability distribution. This distribution is called the sampling distribution model for proportions. Even though we depend on sampling distribution models, we never actually get to see them. We never actually take repeated samples from the same population and make a histogram. We only imagine or simulate them. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1816
17 A Sampling Distribution Model for a Proportion (cont.) Still, sampling distribution models are important because they act as a bridge from the real world of data to the imaginary world of the statistic and enable us to say something about the population when all we have is data from the real world. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1817
18 The Sampling Distribution Model for a Proportion (cont.) Provided that the sampled values are independent and the sample size is large enough, the sampling distribution of ˆp (sample proportion of success) is modeled by a Normal model with Mean: ( ˆp) p Standard deviation: SD( ˆp) pq n Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1818
19 Example p. 434 #15 Based on past experience, a bank believes that 7% of the people who receive loans will not make payments on time The bank has recently approved 200 loans. a) What are the mean and standard deviation of the proportion of clients in this group who may not make timely payments? b) What assumptions underlie you model? Are the conditions met? Explain. c) What s the probability that over 10% of these clients will not make timely payments? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1819
20 Ch 18 Homework p. 432 #3, 5, 7, 9, 16, 22 Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1820
21 What About Quantitative Data? Proportions summarize categorical variables. The Normal sampling distribution model looks like it will be very useful. Can we do something similar with quantitative data? We can indeed. Even more remarkable, not only can we use all of the same concepts, but almost the same model. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1821
22 Quantitative Data Deals with sample means Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1822
23 Simulating the Sampling Distribution of a Mean Like any statistic computed from a random sample, a sample mean also has a sampling distribution. We can use simulation to get a sense as to what the sampling distribution of the sample mean might look like Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1823
24 Means The Average of One Die Let s start with a simulation of 10,000 tosses of a die. A histogram of the results is: Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1824
25 Means Averaging More Dice Looking at the average of two dice after a simulation of 10,000 tosses: The average of three dice after a simulation of 10,000 tosses looks like: Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1825
26 Means Averaging Still More Dice The average of 5 dice after a simulation of 10,000 tosses looks like: The average of 20 dice after a simulation of 10,000 tosses looks like: Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1826
27 Means What the Simulations Show As the sample size (number of dice) gets larger, each sample average is more likely to be closer to the population mean. So, we see the shape continuing to tighten around 3.5 And, it probably does not shock you that the sampling distribution of a mean becomes Normal. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1827
28 The Fundamental Theorem of Statistics The sampling distribution of any mean becomes more nearly Normal as the sample size grows. All we need is for the observations to be independent and collected with randomization. We don t even care about the shape of the population distribution! The Fundamental Theorem of Statistics is called the Central Limit Theorem (CLT). Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1828
29 The Fundamental Theorem of Statistics (cont.) The CLT is surprising and a bit weird: Not only does the histogram of the sample means get closer and closer to the Normal model as the sample size grows, but this is true regardless of the shape of the population distribution. The more skewed, the larger the sample size we need. The CLT works better (and faster) the closer the population model is to a Normal itself. It also works better for larger samples. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1829
30 Assumptions and Conditions The CLT requires essentially the same assumptions we saw for modeling proportions: Independence Assumption: The sampled values must be independent of each other. Sample Size Assumption: The sample size must be sufficiently large. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1831
31 Assumptions and Conditions (cont.) We can t check these directly, but we can think about whether the Independence Assumption is plausible. We can also check some related conditions: Randomization Condition: The data values must be sampled randomly. 10% Condition: When the sample is drawn without replacement, the sample size, n, should be no more than 10% of the population. Large Enough Sample Condition: The CLT doesn t tell us how large a sample we need. For now, you need to think about your sample size in the context of what you know about the population. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1832
32 But Which Normal? The CLT says that the sampling distribution of any mean or proportion is approximately Normal. But which Normal model? For proportions, the sampling distribution is centered at the population proportion. For means, it s centered at the population mean. But what about the standard deviations? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1833
33 But Which Normal? (cont.) The Normal model for the sampling distribution of the mean has a standard deviation equal to SD y n where σ is the population standard deviation. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1834
34 Central Limit Theorem So the normal model dealing with sample means is written like N(, n ) Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1836
35 About Variation The standard deviation of the sampling distribution declines only with the square root of the sample size (the denominator contains the square root of n). Therefore, the variability decreases as the sample size increases. While we d always like a larger sample, the square root limits how much we can make a sample tell about the population. (This is an example of the Law of Diminishing Returns.) Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1837
36 Example p. 437 #49 A waiter believes the distribution of his tips has a model that is slightly skewed to the right, with a mean of $9.60 and a standard deviation of $5.40. a) Explain why you cannot determine the probability that a given party will tip him at least $20. b) Can you estimate the probability that the next 4 parties will tip an average of at least $15? Explain. c) Is it likely that his 10 parties today will tip an average of at least $15? Explain. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1838
37 k Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1839
38 Homework Chapter 18 Homework: p. 432 #31, 33, 37, 50, Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1840
39 The Real World and the Model World Be careful! Now we have two distributions to deal with. The first is the real world distribution of the sample, which we might display with a histogram. The second is the math world sampling distribution of the statistic, which we model with a Normal model based on the Central Limit Theorem. Don t confuse the two! Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1841
40 Sampling Distribution Models Always remember that the statistic itself is a random quantity. We can t know what our statistic will be because it comes from a random sample. Fortunately, for the mean and proportion, the CLT tells us that we can model their sampling distribution directly with a Normal model. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1842
41 Sampling Distribution Models (cont.) There are two basic truths about sampling distributions: 1. Sampling distributions arise because samples vary. Each random sample will have different cases and, so, a different value of the statistic. 2. Although we can always simulate a sampling distribution, the Central Limit Theorem saves us the trouble for means and proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1843
42 What Can Go Wrong? Don t confuse the sampling distribution with the distribution of the sample. When you take a sample, you look at the distribution of the values, usually with a histogram, and you may calculate summary statistics. The sampling distribution is an imaginary collection of the values that a statistic might have taken for all random samples the one you got and the ones you didn t get. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1845
43 What Can Go Wrong? (cont.) Beware of observations that are not independent. The CLT depends crucially on the assumption of independence. You can t check this with your data you have to think about how the data were gathered. Watch out for small samples from skewed populations. The more skewed the distribution, the larger the sample size we need for the CLT to work. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1846
44 What have we learned? Sample proportions and means will vary from sample to sample that s sampling error (sampling variability). Sampling variability may be unavoidable, but it is also predictable! Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1847
45 What have we learned? (cont.) We ve learned to describe the behavior of sample proportions when our sample is random and large enough to expect at least 10 successes and failures. We ve also learned to describe the behavior of sample means (thanks to the CLT!) when our sample is random (and larger if our data come from a population that s not roughly unimodal and symmetric). Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1848
46 Homework Chapter 18 Homework: p. 432 #2(1), 4(3), 6(5), 8(7), 10(9), 16, 22, 30(29), 34, 38, 50, 52 Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1849
47 Example p. 432 #1 See book Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1850
48 Example p. 432 #3 The philanthropic organization in Exercise 1 expects about a 5% success rate when they send fundraising letters to the people on their mailing list. In Exercise 1 you looked at the histograms showing distributions of sample proportions from 1000 simulated mailings for samples of size 20, 50, 100, and 200. The sample statistics from each simulation were as follows: a) According to the CLT, what should the theoretical mean and standard deviation be for these sample sizes? n Mean St. Dev Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1851
49 Example p. 432 #3 The philanthropic organization in Exercise 1 expects about a 5% success rate when they send fundraising letters to the people on their mailing list. In Exercise 1 you looked at the histograms showing distributions of sample proportions from 1000 simulated mailings for samples of size 20, 50, 100, and 200. The sample statistics from each simulation were as follows: b) How close are those n Mean St. Dev theoretical values to what was observed in these simulations? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1852
50 Example p. 432 #3 The philanthropic organization in Exercise 1 expects about a 5% success rate when they send fundraising letters to the people on their mailing list. In Exercise 1 you looked at the histograms showing distributions of sample proportions from 1000 simulated mailings for samples of size 20, 50, 100, and 200. The sample statistics from each simulation were as follows: c) Looking at the histograms n Mean St. Dev in Exercise 1, at what sample size would you be comfortable using the Normal model as an approximation for the sampling distribution? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1853
51 Example p. 432 #3 The philanthropic organization in Exercise 1 expects about a 5% success rate when they send fundraising letters to the people on their mailing list. In Exercise 1 you looked at the histograms showing distributions of sample proportions from 1000 simulated mailings for samples of size 20, 50, 100, and 200. The sample statistics from each simulation were as follows: d) What does the n Mean St. Dev Success/Failure Condition say about the choice you made in part c? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1854
52 Example p. 433 #5 In a large class of introductory Statistics students, the professor has each person toss a coin 16 times and calculate the proportion of his or her tosses that were heads. The students then report their results, and the professor plots a histogram of these several proportions. d) Explain why a Normal model should not be used here. a) What shape would you expect this histogram to be? Why? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1855
53 Example p. 433 #5 In a large class of introductory Statistics students, the professor has each person toss a coin 16 times and calculate the proportion of his or her tosses that were heads. The students then report their results, and the professor plots a histogram of these several proportions. b) Where do you expect the histogram to be centered? c) How much variability would you expect among these proportions? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1856
54 Example p. 433 #7 Suppose the class in Exercise 5 repeats the cointossing experiment. a) The students toss the coins 25 times each. Use the Rule to describe the sampling distribution. b) Confirm that you can use the Normal model here. c) They increase the number of tosses to 64 each. Draw and label the appropriate sampling distribution model. Check the appropriate conditions to justify your model. d) Explain how the sampling distribution model changes as the number of tosses increases. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1857
55 Example p. 433 #9 One of the students in the introductory Statistics class in Exercise 7 claims to have tossed her coin 200 times and found only 42% heads. What do you think of this claim? Explain. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1858
56 Example p. 434 #21 Just before a referendum on a school budget, a local newspaper polls 400 random voters in an attempt to predict whether the budget will pass. Suppose that the budget actually has the support of 52% of the voters. What s the probability the newspaper s sample will lead them to predict defeat (less than 50%)? Be sure to verify that the assumptions and conditions necessary for our analysis are met. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1859
57 Example p. 434 #29 See book Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1860
58 Example p. 432 #31 Researchers measured the Waist Sizes of 250 men in a study on body fat. The true mean and standard deviation of the Waist Sizes for the 250 men are in and in, respectively. In Exercise 29 you looked at the histograms of simulations that drew samples of sizes 2, 5, 10, and 20 (with replacement). The summary statistics for these simulations were as follows: n Mean St. Dev a) According to the CLT, what should the theoretical mean and standard deviation be for each of these sample sizes? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1861
59 Example p. 432 #31 Researchers measured the Waist Sizes of 250 men in a study on body fat. The true mean and standard deviation of the Waist Sizes for the 250 men are in and in, respectively. In Exercise 29 you looked at the histograms of simulations that drew samples of sizes 2, 5, 10, and 20 (with replacement). The summary statistics for these simulations were as follows: n Mean St. Dev b) How close are those theoretical values to what was observed in these simulations? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1862
60 Example p. 432 #31 Researchers measured the Waist Sizes of 250 men in a study on body fat. The true mean and standard deviation of the Waist Sizes for the 250 men are in and in, respectively. In Exercise 29 you looked at the histograms of simulations that drew samples of sizes 2, 5, 10, and 20 (with replacement). The summary statistics for these simulations were as follows: n Mean St. Dev c) Looking at the histograms in Exercise 29, at what sample size would you be comfortable using the Normal model as an approximation for the sampling distribution? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1863
61 Example p. 432 #31 Researchers measured the Waist Sizes of 250 men in a study on body fat. The true mean and standard deviation of the Waist Sizes for the 250 men are in and in, respectively. In Exercise 29 you looked at the histograms of simulations that drew samples of sizes 2, 5, 10, and 20 (with replacement). The summary statistics for these simulations were as follows: d) What about the shape of n Mean St. Dev the distribution of Waist Size explains your choice of sample size in part c? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1864
62 Example p. 436 #33 A college s data about the incoming freshman indicates that the mean of their high school GPAs was 3.4, with a standard deviation of 0.35; the distribution was roughly moundshaped and only slightly skewed. The students are randomly assigned to freshman writing seminars in groups of 25. What might the mean GPA of one of these seminar groups be? Describe the appropriate sampling distribution model shape, center, spread with attention to assumptions and conditions. Make a sketch using the Rule. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1865
63 Example p. 437 #49 A waiter believes the distribution of his tips has a model that is slightly skewed to the right, with a mean of $9.60 and a standard deviation of $5.40. a) Explain why you cannot determine the probability that a given party will tip him at least $20. b) Can you estimate the probability that the next 4 parties will tip an average of at least $15? Explain. c) Is it likely that his 10 parties today will tip an average of at least $15? Explain. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1866
64 k Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1867
65 Example p. 438 #51 A waiter believes the distribution of his tips has a model that is slightly skewed to the right, with a mean of $9.60 and a standard deviation of $5.40. The waiter usually waits on about 40 parties over a weekend of work. a) Estimate the probability that he will earn at least $500 in tips. b) How much does he earn on the best 10% of such weekends? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1868
66 Homework Chapter 18 Homework: p. 432 #2(1), 4(3), 6(5), 8(7), 10(9), 16, 22, 30(29), 32(31), 34, 38, 50(49), 52(51) Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1869
67 Practice 1 Groovy M&M s are supposed to make up 30% of the candies sold. In a large bag of 250 M&M s, what is the probability that we get at least 25% groovy candies? Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1870
68 Practice 2 At birth, babies average 7.8 pounds, with a standard deviation of 2.1 pounds. A random sample of 34 babies born to mothers living near a large factory that may be polluting the air and water shows a mean birth weight of only 7.2 pounds. Is that unusually low? Explain. Copyright 2010, 2007, 2004 Pearson Education, Inc. Slide 1871
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