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Lab 4: Sampling Pre-Reading STAT 200 Objectives To sample from a population using Excel via simple random sampling and via stratified sampling. To see variability and bias in estimation To compare the sampling distribution of the sample mean gotten from a simple random sample to the sampling distribution of the sample mean gotten from a stratified sample To see how to modify an estimator for stratified sampling Sampling and Inference We’ve already encountered the notion of a population and a sample. In order to obtain information about a population, a sample is obtained and used to make inference about the population. Descriptive statistics are used to describe and understand the characteristics within the sample. But we want to use these statistics to understand the population from which the sample was taken. This is called statistical inference . One form of inference is estimation of a parameter. In the context of sampling, a parameter is simply a numerical characteristic of the population. Some common examples are total, average and median. An estimate of a parameter is a statistic from a sample, a statistic used to “guess” the value of the unknown population parameter. In most cases, a sample is obtained in some random fashion. In this case, if we were to sample over and over again, each sample would differ from the others. This is called sampling variability . Consequently, the estimate obtained will vary from sample to sample. An advantage of random sampling is that it allows us to use probability theory to quantify

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