Lecture11ch5_7

# Lecture11ch5_7 - Stat 350 Lecture 11 5.6 Describing...

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Stat 350 Lecture 11 5.6 Describing Sampling Distributions

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The Central Limit Theorem The sampling distribution of the mean can be approximated by a normal distribution when the sample size n is sufficiently large, irrespective of the sample of the population distribution. In general, n >= 30 is large enough The less symmetric a population is, the larger the sample size will have to be to ensure normality of the mean (exponential distribution requires n=40)
Mean and Standard Deviation of the Sampling Distribution of Let be the sample mean of a random sample from a population with mean and standard deviation , then and The above two equations hold regardless of the particular form of the population distribution is also called the standard error of The standard error of decreases as n increases X X 1 ,, n XX X  X n X X X

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Sampling Distribution of the Sample Proportion Parameter of Interest: π : the proportion of the population that has the characteristic of interest Numerically Code Information X=1 if a member has a specific characteristic X=0 if a member does not
Sampling Distribution of the Sample Proportion Parameter of Interest: π : the proportion of the population Numerically Code Information X=1 if a member has a specific characteristic X=0 if a member does not Mass function for X: Population parameters:

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## This note was uploaded on 04/23/2011 for the course STAT 350 taught by Professor Staff during the Spring '08 term at Purdue.

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Lecture11ch5_7 - Stat 350 Lecture 11 5.6 Describing...

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