Intro Stat Weekly Notes 7
Intro Stat Weekly Notes 7 TMATH 110 C
University of Washington Tacoma
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This 0 page Class Notes was uploaded by Qihua Wu on Sunday November 15, 2015. The Class Notes belongs to TMATH 110 C at University of Washington Tacoma taught by KENNEDY,MAUREEN C. in Fall 2015. Since its upload, it has received 24 views. For similar materials see Intro Stat Applications in Math at University of Washington Tacoma.
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Date Created: 11/15/15
Central Limit Theory As sample size increases the sample size mean approaches the population mean Works for normal distributions or for sample size greater than 30 Given a sample size population mean and standard deviation of x and trying to use central limit theory to nd the probability for that certain sample size to have a certain range of mean 1 Find the standard deviation of the sample mean standard error Standard error standard deviation of xsample sizequot5 2 Use the standard error to nd the probability similar with nding the probability for the range of x Given value mean standard error zscore on the table For clari cation of how to use zscore or how to nd range for both table see weekly note 6 Estimation estimating population parameters with sample data Hypothesis testing testing hypothesis about population parameters with sample data Estimator the estimation of unknown population parameter Point estimate estimate population parameter with one value but since it is only a single value it would have errors due to sampling variabilities Point estimate of population mean Sample mean estimation of population mean Sum of all x values sample size Point estimate of population variance Sample varianceestimation of population variance sum of x meanquot2 sample size1 Point estimate of population proportion Estimated population proportion frequency sample size Point estimates of population parameters are only useful when they are unbiased since statisticians need to avoid biases at all cost An unbiased estimator is one that approaches to the value of the population parameter Unbiased estimators Sample mean sample variance sample proportion Biased estimators Sample median sample range sample standard deviation Sample range and sample standard deviation tend to underestimate the population parameter Notice how sample variance is not biased to make sample standard deviation unbiased we can just square it to make it become va ance
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