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PointEstimation

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    Sample Proportions and Point Estimation

     

    Sample Proportions

    Let \(\hat{p}\) be the proportion of successes of a sample from a population whose total proportion of successes is p and let \(\mu_{\hat{p}}\) be the mean of \(\hat{p}\) and \(\sigma_{\hat{p}}\)  be its standard deviation.

    Then

     

     

    The Central Limit Theorem For Proportions

    1. \(\mu_{\hat{p}} = p\) 

    2. \(\sigma_{\hat{p}} = \sqrt{\frac{p(1-p)}{n}}\)

    3.  For n large, p is approximately normal.

     

    Example  

    Consider the next census.  Suppose we are interested in the proportion of Americans that are below the poverty level.  Instead of attempting to find all Americans, Congress has proposed to perform statistical sampling.  We can concentrate on 10,000 randomly selected people from 1000 locations.  We can determine the proportion of people below the poverty level in each of these regions.  Suppose this proportion is .08.  Then the mean for the sampling distribution is

         \(\mu_{\hat{p}} = 0.8 \)

     and the standard deviation is 

         \(\sigma_{\hat{p}} = \sqrt{\frac{0.08*0.92}{10,000}} = 0.0027 \)

     


    Point Estimations

    A Point Estimate is a statistic that gives a plausible estimate for the value in question.  

    Example

            x is a point estimate for m

            s is a point estimate for s

     

    A point estimate is unbiased if its mean represents the value that it is estimating.

     


    Back to the Estimation Home Page

     

    PointEstimation is shared under a not declared license and was authored, remixed, and/or curated by LibreTexts.

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