Answer:
The standard deviation of the sampling distribution is 0.0122 = 1.22%
Explanation:
Central Limit Theorem
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean
and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
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
and standard deviation
![s = \sqrt{(p(1-p))/(n)}](https://img.qammunity.org/2021/formulas/mathematics/college/4g01jif87kw0yiycg79zy61z1uo268l9th.png)
A survey asks a random sample of 1500 adults in Ohio
This means that
![n = 1500](https://img.qammunity.org/2021/formulas/mathematics/college/dntd13ub2fkwn48lpc3qzi8iddnp8c1xmk.png)
34% of all adults in Ohio support the increase.
This means that
![p = 0.34](https://img.qammunity.org/2021/formulas/mathematics/college/cds94q70znogtdbkqxfpn7y6jz05dcd6z7.png)
The standard deviation of the sampling distribution is
![s = \sqrt{(0.34*0.66)/(1500)} = 0.0122](https://img.qammunity.org/2021/formulas/mathematics/college/lplysw405szkbaa1pozc7t6qsbfzrum1v9.png)
The standard deviation of the sampling distribution is 0.0122 = 1.22%