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Lecture-9-Summer2010 - LECTURE 9 Random Sampling and Point...

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LECTURE 9 Random Sampling and Point Estimation This lecture covers material on Simple Random Sampling for finite and infinite populations, and Point Estimation. Microsoft Excel is used for some applications. Read: Chapter 7, Sections 7.1 through 7.3. 1
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This lecture introduces the idea of statistical inference, an important reason for managers to use statistics. Before we start our discussion, are a few terms that we need to revisit and explain. They are: Population: The set of all elements of interest in a particular study. The number of elements in the population is denoted by the letter N. For example, the population of software designers includes all those who work in the software design area. It might be interesting to find out some information about software designers, such as the distribution of their salaries, their mean or average annual salary and the standard deviation of salaries. When we calculate the values of the population such as the mean or the variance, those values are called parameters . The mean of a population variable is denoted by μ and the standard deviation is denoted by σ . If we could contact all software designers and ask them their salaries, we would be able to determine the distribution of salaries. We could plot the salaries in a frequency plot showing how many software designers got a certain salary amount. The distribution might be bimodal, having a lot of software designers with high salaries and another large group with low salaries, and a few with average salaries. Or the distribution might be normal, with the plot of salaries resembling a bell-shaped curve. In any case, whatever the distribution looks like, we would call it the population distribution. However there is a problem in doing this. It is not really likely that we would be able to ask all software designers their salaries. What we would
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