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Class_9 - The Central Limit Theorem and the Normal...

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The Central Limit Theorem The Central Limit Theorem and and the Normal Distribution the Normal Distribution

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Recapitulation from Last Time Recapitulation from Last Time 1. Statistical inference involves generalizing from a sample to a (statistical) universe . 2. Statistical inference is only possible with random samples . 3. Statistical inference estimates the probability that a sample result could be due to chance ( in the selection of the sample ). 4. Sampling distributions are the keys that connect (known) sample statistics and (unknown) universe parameters . 5. Alpha (significance) levels are used to identify critical values on sampling distributions.
The Central Limit Theorem The Central Limit Theorem If repeated random samples of size N are drawn from a population that is normally distributed along some variable Y , having a mean μ and a standard deviation σ , then the sampling distribution of all theoretically possible sample means will be a normal distribution having a mean μ and a standard deviation given by [Sirkin (1999), p. 239] n s Y ˆ

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This note was uploaded on 02/04/2008 for the course PPD 404 taught by Professor Velez during the Fall '07 term at USC.

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Class_9 - The Central Limit Theorem and the Normal...

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