Lecture 2, assignment 2 - Lecture 2 Notes , Central Limit...

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Lecture 2 Notes , Central Limit Theorem – Certain statistics (sample means, for example) are normally distributed, If the distribution is symmetrical, a small sample the larger the sample size the more normal the distribution will become. An Exercise to demonstrate the Central Limit Theorem : X P(X) (X - µ x ) 2 •P(Xi) P() 1 1/3 4/3 3 1/3 0 3/9 5 1/3 4/3 2/9 µ= Σ (Xi•P(Xi)) = 3 σ 2 = E(X-µ x ) 2 = Σ (X - µ x ) 2 •P(Xi) = 8/3 1/9 Sample Size 2 1 2 3 4 5 Notice that with a sample size of only two, the distribution of sample means can be approximated by the normal distribution. That is to say, sample means quickly take on the shape of the normal even when the sample size is very small. Notice, however, that the original distribution of the variable was symmetrical. If the underlying distribution is highly skewed, then it would take a much larger sample size for the distribution of sample means to become normal. P() (P) ( - µ) 2 ( - µ) 2 •P() 1, 1 1 1 1/9 1/9 4 4/9 1, 3 2 2 2/9 4/9 1 2/9 1, 5 3 3 3/9 9/9 0 0 3, 1 2 4 2/9 8/9 1 2/9 3, 3 3 5 1/9 5/9 4 4/9 3, 5 4 µ = 3 σ 2 = 4/3 5, 1 3 5, 3 4 5, 5 5 σ 2 = Σ (- µ ) 2 P() σ 2 = (8/3)/2 = 4/3 1
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E() = µ x (the population mean, regardless of population size) E() = Σ (Xi•P(Xi)) and Σ •P() = µ If you increase sample size, the variance of will decease, even though the possible values that . may take on will increase. There will
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This note was uploaded on 08/24/2010 for the course MATH 267 taught by Professor Chandrasekhar during the Summer '10 term at Anna University Chennai - Regional Office, Coimbatore.

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Lecture 2, assignment 2 - Lecture 2 Notes , Central Limit...

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