Answer:
The standard deviation of the sampling distribution is 0.0122 = 1.22%
Step-by-step explanation:
Central Limit Theorem
The Central Limit Theorem estabilishes 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]
A survey asks a random sample of 1500 adults in Ohio
This means that [tex]n = 1500[/tex]
34% of all adults in Ohio support the increase.
This means that [tex]p = 0.34[/tex]
The standard deviation of the sampling distribution is
[tex]s = \sqrt{\frac{0.34*0.66}{1500}} = 0.0122[/tex]
The standard deviation of the sampling distribution is 0.0122 = 1.22%