Answer:
The sampling distribution of the sample proportion of adults who have credit card debts of more than $2000 will be normally distributed with mean = 0.38 and standard deviation of 0.034.
Step-by-step explanation:
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, the sampling distribution will be normally distributed with mean [tex]\mu = p[/tex] and standard deviation [tex]s = \sqrt{\frac{p(1-p)}{n}}[/tex]
In this problem, we have that:
[tex]n = 200, p = 0.38[/tex]
So
[tex]s = \sqrt{\frac{p(1-p)}{n}} = \sqrt{\frac{0.38*0.62}{200}} = 0.034[/tex]
The sampling distribution of the sample proportion of adults who have credit card debts of more than $2000 will be normally distributed with mean = 0.38 and standard deviation of 0.034.