Proportion Estimation: Difference between revisions
From Rice Wiki
(Blanked the page) Tags: Blanking Manual revert |
No edit summary Tag: Manual revert |
||
Line 1: | Line 1: | ||
Proportion estimation is another common task for sample statistics. | |||
We have sample proportion | |||
<math> | |||
\hat{p} = \frac{y}{n} | |||
</math> | |||
where <math>y</math> is the number of subjects in the sample with a | |||
particular trait, and <math>n</math> is the sample size. | |||
We have | |||
<math> | |||
\mu_\hat{p} = p, \sigma_\hat{p} = \sqrt{\frac{p (1 - p)}{n}} | |||
</math> | |||
and standard error | |||
<math> | |||
SE = \sqrt{\frac{\hat{p} (1 - \hat{p})}{n}} | |||
</math> | |||
= Wilson-Adjusted CI for p = | |||
''Correcting'' the sample proportion narrows the confidence interval. We | |||
do this with the '''Wilson-Adjusted estimate''' for <math>p</math> | |||
<math> | |||
\widetilde{p} | |||
</math> | |||
[[Category:Sample Statistics]] |
Revision as of 02:01, 16 March 2024
Proportion estimation is another common task for sample statistics.
We have sample proportion
where is the number of subjects in the sample with a particular trait, and is the sample size.
We have
and standard error
Wilson-Adjusted CI for p
Correcting the sample proportion narrows the confidence interval. We do this with the Wilson-Adjusted estimate for