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Statistics for Decision- Making in Business - Maricopa Community ...

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DULY CAUTIONED: The assumptions here are the same as <strong>for</strong> bootstrapp<strong>in</strong>g with ̅: a<br />

random sample is drawn from the population and is representative of the population. If not, the<br />

sample is worthless, <strong>in</strong> any case.<br />

6.3.2 Confidence Interval <strong>for</strong> ̂ Us<strong>in</strong>g Theoretical Results<br />

Without provid<strong>in</strong>g the <strong>in</strong>tuition <strong>for</strong> this method, we will simply state the results <strong>for</strong> the CLT<br />

perta<strong>in</strong><strong>in</strong>g to the sampl<strong>in</strong>g distribution of ̂:<br />

Central Limit Theorem <strong>for</strong> ̂<br />

The sampl<strong>in</strong>g distribution of ̂ (which is really just an average of 0‟s and 1‟s) is approximately<br />

normal just as long as (similar idea as <strong>for</strong> the standard CLT).<br />

With<br />

̂<br />

( ̂)<br />

, ̂-<br />

, ̂- √<br />

̂( ̂)<br />

NOTE: the standard deviation is often referred to as the marg<strong>in</strong> of error <strong>in</strong> polls.<br />

The results above state that,<br />

1. the average proportion of the sampl<strong>in</strong>g distribution is the true population proportion.<br />

2. The standard deviation of proportions of the sampl<strong>in</strong>g distribution is the above, complex,<br />

calculation.<br />

AS LONG AS ̂ and ( ̂) , both of which are<br />

true statements. We can now proceed:<br />

Here, we get to use the standard normal distribution to calculate the number of standard<br />

deviations correspond<strong>in</strong>g to the desired <strong>in</strong>terval. So, we know that:<br />

̂<br />

<strong>Statistics</strong> <strong>for</strong> <strong>Decision</strong>-<strong>Mak<strong>in</strong>g</strong> <strong>in</strong> Bus<strong>in</strong>ess © Milos Podmanik Page 205

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