Chapter11

The Basic Practice of Statistics (Paper) & Student CD

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Sampling distributions BPS chapter 11 © 2006 W. H. Freeman and Company
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Objectives (BPS chapter 11) Sampling distributions Parameter versus statistic The law of large numbers What is a sampling distribution? The sampling distribution of The central limit theorem Statistical process control x
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Reminder: Parameter versus statistic Sample: the part of the population we actually examine and for which we do have data. A s tatistic is a number describing a characteristic of a s ample. We often use a statistic to estimate an unknown population parameter. Population: the entire group of individuals in which we are interested but can’t usually assess directly. A p arameter is a number describing a characteristic of the p opulation. Parameters are usually unknown. Population Sample
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Apply your knowledge Go to page 272 and work on the following problems: 11.1 Effects of caffeine 11.2 Indianapolis voters 11.3 Inspecting bearings
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The law of large numbers Law of large numbers : As the number of randomly-drawn observations ( n ) in a sample increases, the mean of the sample ( ) gets closer and closer to the population mean μ (quantitative variable). the sample proportion ( ) gets closer and closer to the population proportion p (categorical variable). x ˆ p
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Apply your knowledge Go to page 275 and work on the following problems: 11.4 Means in action 11.5 Insurance
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The sampling distribution of a statistic is the distribution of all possible values taken by the statistic when all possible samples of a fixed size n are taken from the population. It is a theoretical idea—we do not actually build it. The sampling distribution of a statistic is the probability distribution of that statistic. Note: When sampling randomly from a given population, the law of large numbers describes what happens when the sample size n is gradually increased. The sampling distribution describes what happens when we take all
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Chapter11 - Sampling distributions BPS chapter 11 2006 W H...

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