Proportion Estimation: Difference between revisions

Revision as of 01:58, 16 March 2024

Proportion estimation is another common task for sample statistics.

${\hat {p}}={\frac {y}{n}}$

where $y$ is the number of subjects in the sample with a particular trait, and $n$ is the sample size.

We have

$\mu _{\hat {p}}=p,\sigma _{\hat {p}}={\sqrt {\frac {p(1-p)}{n}}}$

and standard error

$SE={\sqrt {\frac {{\hat {p}}(1-{\hat {p}})}{n}}}$

@@ Line 1: / Line 1: @@
+Proportion estimation is another common task for sample statistics.
+<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>
+[[Category:Sample Statistics]]