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Answer:
The standard error of the distribution of sample proportions is of 0.014.
Step-by-step explanation:
Central Limit Theorem
The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]s = \frac{\sigma}{\sqrt{n}}[/tex].
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
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]
Consider random samples of size 1200 from a population with proportion 0.65 .
This means that [tex]n = 1200, p = 0.65[/tex]
Find the standard error of the distribution of sample proportions.
This is s. So
[tex]s = \sqrt{\frac{0.65*0.35}{1200}} = 0.014[/tex]
The standard error of the distribution of sample proportions is of 0.014.
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