# IM Ch07 - 136 Instructor's Manual 1 Chapter 7 Statistical...

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136 Instructor's Manual 1 Chapter 7 Statistical Inference and Sampling CHAPTER OVERVIEW AND OBJECTIVES Chapter 6 introduced several continuous probability distributions, including the very important normal distribution. In this chapter, the use of the normal distribution will be extended to problems involving statistical estimation and statistical inference, that is, the estimation of population characteristics (parameters) on the basis of sample information. By the end of the chapter, the student should be able to: 1. Define and distinguish between sample statistics and population parameters. 2. Discuss the Central Limit Theorem and illustrate its use in statistical inference. 3. Construct confidence intervals using both the normal distribution and the Student t distribution. 4. Describe different aspects of sampling and sampling techniques such as: sampling error, finite population correction factor, systematic sampling, stratified sampling, and cluster sampling. 136

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Instructor's Manual Chapter 7 Glossary Central Limit Theorem . A result that states that for large samples, the distribution of the sample mean, 3, is approximately normal regardless of the shape of the sampled population. cluster sample . A sample obtained by randomly selecting groups (clusters) of elements from the population. confidence interval . An interval believed to contain the corresponding population parameter with a specified level of confidence. confidence level . The confidence associated with the ability of a confidence interval to contain the true value of the corresponding parameter. degrees of freedom . A value that specifies which t curve (distribution) is being used from the family of t curves. When constructing a confidence interval for a population mean using the t distribution, the degrees of freedom is n - 1, where n is the sample size. inference . The process of drawing conclusions about a population based on the results of a statistical sample. margin of error (E) . The amount that is added to and subtracted from the sample mean when constructing a confidence interval for the population mean. parameter . A value that describes the population, such as the population mean (3). point estimate . A single value of a sample statistic used as an estimate of a population parameter. population . The set of all possible measurements of interest. sampling distribution . The probability distribution of a sample statistic. sample unit . A collection of elements (cluster) or an individual element selected from the population to be included in a sample. sampling design . A plan that specifies the manner in which the sampling units are to be selected for the sample, such as simple random sampling, systematic sampling, stratified sampling or cluster sampling. sampling frame
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IM Ch07 - 136 Instructor's Manual 1 Chapter 7 Statistical...

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