IntroductiontoSamplingDistributions文件材料.pptVIP

Business Statistics: A Decision-Making Approach, 6e ? 2005 Prentice-Hall, Inc. Business Statistics: A Decision-Making Approach, 6e ? 2005 Prentice-Hall, Inc. Business Statistics: A Decision-Making Approach, 6e ? 2005 Prentice-Hall, Inc. Business Statistics: A Decision-Making Approach 6th Edition Chapter 6Introduction to Sampling Distributions Chapter Goals After completing this chapter, you should be able to: Define the concept of sampling error Determine the mean and standard deviation for the sampling distribution of the sample mean, x Determine the mean and standard deviation for the sampling distribution of the sample proportion, p Describe the Central Limit Theorem and its importance Apply sampling distributions for both x and p _ _ _ _ Example If the population mean is μ = 98.6 degrees and a sample of n = 5 temperatures yields a sample mean of = 99.2 degrees, then the sampling error is Sampling Errors Different samples will yield different sampling errors The sampling error may be positive or negative ( may be greater than or less than μ) The expected sampling error decreases as the sample size increases Sampling Distribution A sampling distribution is a distribution of the possible values of a statistic for a given size sample selected from a population Developing a Sampling Distribution Assume there is a population … Population size N=4 Random variable, x,is age of individuals Values of x: 18, 20,22, 24 (years) A B C D .3 .2 .1 0 18 20 22 24 A B C D Uniform Distribution P(x) x (continued) Summary Measures for the Population Distribution: Developing a Sampling Distribution 16 possible samples (sampling with replacement) Now consider all possible samples of size n=2 (continued) Developing a Sampling Distribution 16 Sample Means Sampling Distribution of All Sample Means 18 19 20 21 22 23 24 0 .1 .2 .3 P(x) x Sample Means Distribution 16 Sample Means _ Developing a