2.2.10_Lec 7

2.2.10_Lec 7 - Lecture 7 Sampling Distributions (contd) 1...

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Lecture 7 Sampling Distributions (cont’d) 1
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Lecture 7 Outline . entral Limit Theorem 1. Central Limit Theorem 2. Sampling Distribution of the Difference between Two Sample Means 3. Sampling Distribution of the Sample Proportion 4. Sampling Distribution of the Difference between Two Sample Proportions 5. Review of Sampling Distributions 2
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1. Central Limit Theorem (CLT) Given a population of any non-normal distribution with mean, μ , and finite variance σ 2 , the sampling istribution of the sample mean computed from distribution of the sample mean, computed from all possible samples of size n from this population, will be approximately normal with mean, μ , and variance σ 2 /n when the sample size is large. - In most practical situations, the CLT applies when n is > 25 - The approximation to normality becomes better as n increases and the population distribution is closer to normal. 3
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1. Central Limit Theorem (cont’d) σ 4
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ampling istribution ifference 2. Sampling Distribution: Difference Between Two Sample Means Given two normally distributed populations: ± Population 1 : 9 Size= N 1 , mean = μ 1 ,standard deviation = σ 1 ± Population 2 : 9 Size= N , mean = μ , standard deviation = σ 2 2 2 Independent Samples: amples f ize om opulation ± Samples of size n 1 from Population 1: 9 Size= n 1 , mean = μ 1 , standard deviation = σ 1 / n 1 ± Samples of size n 2
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This note was uploaded on 02/24/2010 for the course BIOS 600 taught by Professor Staff during the Spring '08 term at UNC.

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2.2.10_Lec 7 - Lecture 7 Sampling Distributions (contd) 1...

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