# Chap_8 - Chapter 8 Sampling Methods and the Central Limit...

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Chapter 8 Sampling Methods and the Central Limit Theorem

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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, Describe the Central Limit Theorem and its importance Apply sampling distributions for x x
Statistical Sampling Items of the sample are chosen based on known or calculable probabilities Probability Samples Simple Random Systematic Stratified Cluster

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Simple Random Samples Every individual or item from the population has an equal chance of being selected Selection may be with replacement or without replacement Samples can be obtained from a table of random numbers or computer random number generators
Stratified Samples Population divided into subgroups (called strata ) according to some common characteristic Simple random sample selected from each subgroup Samples from subgroups are combined into one Population Divided into 4 strata Sample

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Sampling Error Sample Statistics are used to estimate Population Parameters ex: X is an estimate of the population mean, μ Problems: Different samples provide different estimates of the population parameter Sample results have potential variability, thus sampling error exits
Calculating Sampling Error Sampling Error: The difference between a value (a statistic) computed from a sample and the corresponding value (a parameter) computed from a population Example: (for the mean) where: μ - x Error Sampling mean population μ sample x

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## This note was uploaded on 03/22/2012 for the course FINA 210 taught by Professor Dakroub during the Spring '12 term at American University in Cairo.

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Chap_8 - Chapter 8 Sampling Methods and the Central Limit...

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