Answer:
[tex] E(\hat p) = \mu_{\hat p} =p =0.46[/tex]
[tex] Sd(\hat p) = \sigma_{\hat p}= \sqrt{\frac{p(1-p)}{n}} = \sqrt{\frac{0.46*(1-0.46)}{2000}}= 0.011 \approx 0.01[/tex]
Step-by-step explanation:
Previous concepts
A confidence interval is "a range of values that’s likely to include a population value with a certain degree of confidence. It is often expressed a % whereby a population means lies between an upper and lower interval".
The margin of error is the range of values below and above the sample statistic in a confidence interval.
Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".
The population proportion have the following distribution
[tex]p \sim N(p,\sqrt{\frac{p(1-p)}{n}})[/tex]
Solution to the problem
For this case we can assume that the distribution for the sample proportion is given by:
[tex] \hat p \sim N (p,\sqrt{\frac{p(1-p)}{n}})[/tex]
Where the mean is given by:
[tex] E(\hat p) = \mu_{\hat p} =p =0.46[/tex]
And the standard deviation is given by:
[tex] Sd(\hat p) = \sigma_{\hat p}= \sqrt{\frac{p(1-p)}{n}} = \sqrt{\frac{0.46*(1-0.46)}{2000}}= 0.011 \approx 0.01[/tex]