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Week_7_Central_Limit_Theorem

# Week_7_Central_Limit_Theorem - Week 7 Sampling...

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C:\Documents and Settings\AYounger\My Documents\Teaching\Math Stats\Lecture Notes\Week 7 Central Limit Theorem.docx 1 Week 7 Sampling Distributions & the Central Limit Theorem (WMS Ch 7) 1 INTRODUCTION Chapter 5 was an important turning point. Chapter 2 introduced the ideas of random events and of a mathematical “probability measure”. Chapters 3 through 5 investigated a wide variety of “theoretical distributions” appropriate to different sorts of random experiment. Starting with Chapter 7, we now begin to focus more closely on “statistics”. These are various functions of the observed values of random variables found in our sample data. Eventually, we use them to infer certain things about the “population” from which they were drawn. So, from here on, you should feel that there is a more obvious “practical” side to the theory than may have been obvious hitherto. There is also a good deal more opportunity to carry out simulations and analyses using R (or whatever other favourite software you may prefer). On the other hand, we now must be very careful to understand the differences between sample and population distributions. Chapter 7 is also important because it introduces the Central Limit Theorem – an idea that is central to applied statistics and econometrics as you go forward. We have skipped Chapter 6 in the interest of time. But in the following notes you will find explanations of a few things from Chapter 6 that we really need. 2 SAMPLING AND POPULATION DISTRIBUTIONS The basic situation is illustrated: The basic idea: Draw a random sample of n observations from the population Use those observations to calculate an estimator for the population statistic Our concern is with the error of the estimate If we can find the probability distribution of the estimator , we can calculate the probability of error Key provisos: Sampling is random Pop N large relative to sample n We treat the observations as independent and identically distributed (iid) random variables. Population Mean: µ St Dev: σ Sample Mean: Y μ St Dev: Y σ

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