The percentage of married couples who own a single family home is 33% for a given population. A financial analyst is interested in the impact that home mortgages have on a couple's finances. As the financial analyst sets up a study, they are curious about the impact of sample size. What is the standard error of the sampling distribution of sample proportions for samples of size n=200, n=300, and n=400? Round all answers to the nearest thousandths if applicable.

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]

The percentage of married couples who own a single family home is 33% for a given population.

This means that [tex]p = 0.33[/tex]

Samples of 200:

[tex]s = \sqrt{\frac{0.33*0.67}{200}} = 0.033[/tex]

For samples of size n=200, the standard error is of 0.033.

Samples of 300:

[tex]s = \sqrt{\frac{0.33*0.67}{300}} = 0.027[/tex]

For samples of size n=300, the standard error is of 0.027.

Samples of 400:

[tex]s = \sqrt{\frac{0.33*0.67}{400}} = 0.024[/tex]

For samples of size n=400, the standard error is of 0.024.