pp.141-148

# pp.141-148 - 142 Interval versus Point Estimation So far,...

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Interval versus Point Estimation So far, we've only looked at one type of estimation-- point estimation. We've used summary statistics as point estimates for population parameters. Some examples are shown below: Parameters Summary Statistics μ         x p p σ s Desirable properties of point estimates: Unbiasedness— A point estimate is unbiased if it’s  expected value (i.e., its mean) is equal to the  parameter it estimates.  Therefore x is an unbiased estimate of μ and p is an unbiased estimate of p. Efficiency —one unbiased estimator is ‘efficient’ relative to another unbiased estimator if it has less variability associated with it. Consistency —an estimator is consistent if, as the sample size gets larger, the variance of the estimator decreases. 142

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Interval estimates , also called confidence Intervals allow us to state how certain (confident) we are that the parameter falls within a given distance from the point estimate. A confidence interval is a range of values that has a pre-specified probability (confidence) of including the parameter.

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## This note was uploaded on 06/06/2011 for the course MGSC 291 taught by Professor Rollins during the Fall '09 term at South Carolina.

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pp.141-148 - 142 Interval versus Point Estimation So far,...

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