Answer:
a) Mean 0.11 and standard deviation 0.0044.
b) Mean 0.11 and standard deviation 0.0099.
c) Mean 0.11 and standard deviation 0.0198
Step-by-step explanation:
Central Limit Theorem:
For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean [tex]\mu = p[/tex] and standard deviation [tex]s = \sqrt{\frac{p(1-p)}{n}}[/tex]
In this question:
[tex]p = 0.11[/tex]
a. For a random sample of size n equals 5000.
Mean:
[tex]\mu = p = 0.11[/tex]
Standard deviation:
[tex]s = \sqrt{\frac{0.11*0.89}{5000}} = 0.0044[/tex]
Mean 0.11 and standard deviation 0.0044.
b. For a random sample of size n equals 1000.
Mean:
[tex]\mu = p = 0.11[/tex]
Standard deviation:
[tex]s = \sqrt{\frac{0.11*0.89}{1000}} = 0.0044[/tex]
Mean 0.11 and standard deviation 0.0099.
c. For a random sample of size n equals 250.
Mean:
[tex]\mu = p = 0.11[/tex]
Standard deviation:
[tex]s = \sqrt{\frac{0.11*0.89}{250}} = 0.0198[/tex]
Mean 0.11 and standard deviation 0.0198