Chapter 10 & 11.docx
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Department
Operations Management and Information System
Course
OMIS 2010
Professor
Alan Marshall
Semester
Fall

Description
Introduction to EstimationConcepts of EstimationCan make inferences about populations through estimations and hypothesis testing Estimation determine approx value of population parameter on basis of sample statisticPoint and Interval EstimatorsPoint estimator draws inferences about apopulation by estimating value of unknown parameter using single value or pointoCertain that estimate will be wrong probability that a continuous random variable will equal a specific value is 0oOften nee to know how close estimator is to the parameteroPoint estimators dont have capacity to reflect effects of larger sample sizesInterval estimator draws inferences about population by estimating value of unknown parameter using an interval oAffected by sample size1Unbiased estimator estimator whose expected value is equal to that parameter oIf taking infiniteof samples and calculate value of estimator in each sample average value of estimators wouldparameter oDoes not tell us how closely the estimator is to the parameter2Consistency difference between estimator and parameter grows smaller as sample size grows largeroMeasure used to gauge closeness is variance or standard deviationoX bar is consistent estimator of mew because variance of X bar is theta squarednoAs n grows larger variance of X bar grows smaller 3Relative Efficiency if there are two unbiased estimators of a parameter the one whose variance is small has a relative efficiency Estimating Population mean when population and Standard Deviation is KnownInterpreting Confidence Interval EstimateConfidence level is probability that the interval includes the actual value of mew 1a is set close to one between 090099
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