Ch10 - Chapter 10 Introduction to Estimation 1 10.1...

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1 Introduction to Estimation Chapter 10
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2 10.1 Introduction Statistical inference is the process by which we acquire information about populations from samples. There are two types of inference: Estimation Hypotheses testing
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3 10.2 Concepts of Estimation The objective of estimation is to determine the value of a population parameter on the basis of a sample statistic. There are two types of estimators: Point Estimator Interval estimator
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4 Point Estimator A point estimator draws inference about a population by estimating the value of an unknown parameter using a single value or point.
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5 Population distribution Point Estimator Parameter ? Sampling distribution A point estimator draws inference about a population by estimating the value of an unknown parameter using a single value or point. Point estimator
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6 An interval estimator draws inferences about a population by estimating the value of an unknown parameter using an interval. Interval estimator Population distribution Sample distribution Parameter Interval Estimator
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7 Selecting the right sample statistic to estimate a parameter value depends on the characteristics of the statistic. Estimator’s Characteristics Estimator’s desirable characteristics: Unbiasedness: An unbiased estimator is one whose expected value is equal to the parameter it estimates. Consistency: An unbiased estimator is said to be consistent if the difference between the estimator and the parameter grows smaller as the sample size increases. Relative efficiency: For two unbiased estimators, the one with a smaller variance is said to be relatively efficient.
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8 10.3 Estimating the Population Mean when the Population
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This note was uploaded on 06/06/2011 for the course ADMS 2320 taught by Professor Rochon during the Spring '08 term at York University.

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Ch10 - Chapter 10 Introduction to Estimation 1 10.1...

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