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Understanding Sampling Distributions in Inferential Statistics

This chapter explores inferential statistics, sampling distributions, and the central limit theorem. Learn about sampling error, mean of sample means, standard error, and various sampling methods.

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Understanding Sampling Distributions in Inferential Statistics

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  1. Chapter 6Sampling Distribution Prepared by Chhay Phang H/P: 012 84 01 02 E-mail:phangg2002@yahoo.com

  2. Chapter Blueprint

  3. 6.1 INTRODUCTION Inferential Statistics Inferential Statistics involves the use of statistic to form a conclusion, or inference, about the corresponding parameter.

  4. 6.2 Sampling Distributions Sampling Error The difference between population parameter and sample statistic used to estimate the parameter Sampling Distribution A all of possible values for a statistic and probability associate with each value.

  5. A. The Mean of the Sample Means B.The Variance and the Standard Error of the Sampling Distribution Variance

  6. Standard Error Standard Error & Standard Deviation

  7. The finite population correction factor (fpc) C.The Impact of Sample size on Standard Error D.The Central Limit Theorem Central Limit Theorem as n large, the distribution of sample mean will approach a normal distribution with a mean =  and a standard error

  8. 6.4 Using the Sampling Distribution 6.5 Sampling Distribution Proportions The expected value of the Sampling distribution

  9. Standard Error The finite population correction factor (fpc) Using the Sampling Distribution

  10. 6.6 Sampling Methods A.Simple Random Sample B.Systematic Sampling C.Stratified Sampling D.Cluster Sampling

  11. Chapter Exercises 1,2,6,8,9,10,11,20 Thank You!

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