Download presentation

Presentation is loading. Please wait.

Published byShea Crocker Modified over 2 years ago

1
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 18 Sampling Distribution Models

2
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 2 Modeling the Distribution of 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. What would the histogram of all the sample proportions look like?

3
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 3 Modeling the Distribution of Sample Proportions (cont.) It turns out that the histogram is ____________, ______________, and ___________________. More specifically, it’s an amazing and fortunate fact that a __________ model is just the right one for the histogram of sample proportions. To use a Normal model, we need to specify its mean and standard deviation. The mean of this particular Normal is at __.

4
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 4 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

5
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 5 Modeling the Distribution of Sample Proportions (cont.) A picture of what we just discussed is as follows:

6
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 6 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 ________________. Just how big of a sample do we need? This will soon be revealed…

7
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 7 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 sampled values must be ____________ _____________________. 2. The sample size, n, must be ____________.

8
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 8 Assumptions and Conditions (cont.) Assumptions are hard—often impossible—to check. That’s why we ____________ them. Still, we need to check whether the assumptions are ________________ 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 ________________and the ____________________ _________________.

9
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 9 Assumptions and Conditions (cont.) 1.10% condition: If sampling has not been made with replacement, then the sample size, n, must be _________________10% of the population. 2.Success/failure condition: The sample size has to be big enough so that both ____ and ___ are greater than ____. So, we need a large enough sample that is not too large.

10
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 10 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 quantity that has a ___________. This distribution is called the __________________ __________ 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.

11
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 11 A Sampling Distribution Model for a Proportion (cont.) Still, sampling distribution models are important because they act as a bridge from the __________________to the imaginary world of ________________and enable us to say something about the _____________ when all we have is data from the ______________.

12
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 12 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 is modeled by a Normal model with Mean: Standard deviation:

13
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Ex. 1 Draw a Normal Model for the sample proportions based on the 68-95-99.7 Rule for the following. Of all cars on the interstate, 80% exceed the speed limit. What proportion of speeders might we see among the next 50 cars? Slide 18- 13

14
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Ex. 2 “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? Slide 18- 14

15
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Assignment P.428 #2, 4, 6, 7, 9, 10, 11, 13, 16, 17, 18, 20 Slide 18- 15

16
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 18 Sampling Distribution Models (2)

17
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 17 Simulating the Sampling Distribution of a Mean Like any statistic computed from a random sample, a sample mean also has a ___________ ______________. We can use simulation to get a sense as to what the sampling distribution of the sample mean might look like… See _______

18
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 18 Means – What the Simulations Show As the sample size (number of dice) gets larger, each sample average is more likely to be ______ _____________________________. So, we see the shape continuing to tighten around _____ And, it probably does not shock you that the sampling distribution of a mean becomes __________.

19
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 19 The Fundamental Theorem of Statistics The sampling distribution of ____ mean becomes _________ as the sample size _________. All we need is for the observations to be ______________ and collected with __________________. We don’t even care about the ________ of the population distribution! The Fundamental Theorem of Statistics is called the ____________________________.

20
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 20 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 _________ _________ as the sample size grows, but this is true regardless of the ________ of the population distribution. The CLT works better (and faster) the closer the population model is to a _________ itself. It also works better for ________ samples. http://onlinestatbook.com/stat_sim/sampling_dist/index.html

21
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 21 The Fundamental Theorem of Statistics (cont.) The Central Limit Theorem (CLT) The mean of a random sample has a _________ ___________ whose shape can be ___________ by a Normal model. The larger the sample, the ______________________________________.

22
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 22 Diminishing Returns The standard deviation of the sampling distribution declines only with the ____________ of the sample size. While we’d always like a larger sample, the _____________________ how much we can make a sample tell about the population. (This is an example of the Law of Diminishing Returns.)

23
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 23 Assumptions and Conditions The CLT requires remarkably few assumptions, so there are few conditions to check: 1.Random Sampling Condition: The data values must be ________________ or the concept of a sampling distribution makes no sense. 2.Independence Assumption: The sample values must be ____________. (When the sample is drawn without replacement, check the _____________…) 3.Large Enough Sample Condition: There is no _____ _______________ rule.

24
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 24 Standard Error Both of the sampling distributions we’ve looked at are Normal. For proportions For means

25
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 25 Standard Error (cont.) When we don’t know p or σ, we’re stuck, right? Nope. We will use ______________________ to estimate these population parameters. Whenever we ________________ the standard deviation of a sampling distribution, we call it a _____________________.

26
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 26 Standard Error (cont.) For a sample proportion, the standard error is For the sample mean, the standard error is

27
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Ex. 3 Speeds of cars on a highway have mean 52 mph and standard deviation 6 mph, and are likely to be skewed to the right (a few very fast drivers). Describe what we might see in random samples of 50 cars.

28
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Ex. 4 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?

29
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Assignment P.429 #21, 23-27, 29-31, 37, 39 Bring in 10 pennies that are in current circulation (no collections or antiques allowed)

30
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 18 Sampling Distribution Models (3)

31
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 31 Sampling Distribution Models Always remember that the statistic itself is a __________________. We can’t know what our ____________ will be because it comes from a random sample. Fortunately, for the mean and proportion, the CLT tells us that we can model their _____________ ___________ directly with a ________________.

32
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 32 Sampling Distribution Models (cont.) There are two basic truths about sampling distributions: 1. Sampling distributions arise because _______________. Each random sample will have different cases and, so, a different value of the ____________. 2. Although we can always simulate a _______ __________, the Central Limit Theorem saves us the trouble for means and proportions.

33
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 33 The Process Going Into the Sampling Distribution Model

34
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 34 What Can Go Wrong? Don’t confuse the __________________ with the ______________________. When you take a sample, you look at the ______________ of the values, usually with a _______________, and you may calculate ____________________. The sampling distribution is ______________ _____________ of the values that a statistic might have taken for all random samples—the one you got and the ones you didn’t get.

35
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 35 What Can Go Wrong? (cont.) Beware of observations that are _____________. The CLT depends crucially on the assumption of _______________. You can’t check this with your data—you have to think about _________________________. Watch out for ________ samples from ________ populations. The more _________ the distribution, the __________the sample size we need for the CLT to work.

36
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 36 What have we learned? Sample proportions and means will vary from sample to sample—that’s _______________ (_________________). Sampling variability may be _____________, but it is also _______________!

37
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 18- 37 What have we learned? (cont.) We’ve learned to describe the behavior of sample proportions when our sample is __________ and _____________ to expect at least ___ successes and failures. We’ve also learned to describe the behavior of sample means (thanks to the CLT!) when our sample is ____________ (and ___________ if our data come from a population that’s not roughly ___________ and _____________).

38
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Ex. 5 An entrepreneur has two stores. The toy store has mean weekly sales of $3740 with a SD of $120. The drug store has mean weekly sales of $3850 and a standard deviation of $150. a) What is the probability that the combined mean weekly sales for both stores will be greater than $7500? b) What is the probability that on any given week the sales for the toy store will be greater than that of the drug store?

39
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Assignment P.429 #12, 14, 32, 34-36, 38, 40-42

Similar presentations

OK

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

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

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on rational numbers for class 8 Ppt on thermal power plant in india Ppt on child labour free download Ppt on fdi in india 2013 Ppt on asp dot net project Ppt on first conditional worksheets Ppt on kotak life insurance Ppt on model united nations Ppt on artificial intelligence techniques for electrical applications Ppt on brand marketing quotes