Topic_6.0___Statistical_Inference___Estimation

Topic_6.0___Statistical_Inference___Estimation -...

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Statistical Inference – Estimation Ash Genaidy
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Introduction This topic deals with how to use sample data to estimate population parameters, focusing on: The population mean (μ) for quantitative variables. The population proportion (π) for qualitative variables.
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Introduction Statistical inference uses sample data to form two types of estimators of parameters. A point estimate consists of a single number, calculated from the data, that is, the best single guess for the parameter. An interval estimate consists of a range of numbers around the point estimate, within which the parameter is believed to fall. An interval estimate helps us gauge the probable accuracy of a sample point estimate.
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Presentation Outline Point Estimate Mean Proportion Confidence Interval Mean Proportion
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Point Estimation - Mean Definition A point estimator of a parameter is a sample statistic that predicts the value of the parameter. Properties of a point estimator A point estimator is unbiased if its sampling distribution centers around the parameter in the sense that the parameter is the mean of the distribution. A biased estimator tends to either underestimate or overestimate the parameter. A second preferable property for an estimator is a small sampling error compared with other estimators. An estimator, whose standard error is smaller than those of other potential estimators, is said to be efficient .
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Sample mean is the point estimator of population mean μ . The
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Topic_6.0___Statistical_Inference___Estimation -...

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