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reycaden
06.04.2021 •
Mathematics
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.
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Ответ:
For samples of size n=200, the standard error is of 0.033.
For samples of size n=300, the standard error is of 0.027.
For samples of size n=400, the standard error is of 0.024.
Step-by-step 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{\frac{p(1-p)}{n}}](/tpl/images/1241/0968/73804.png)
The percentage of married couples who own a single family home is 33% for a given population.
This means that![p = 0.33](/tpl/images/1241/0968/98b70.png)
Samples of 200:
For samples of size n=200, the standard error is of 0.033.
Samples of 300:
For samples of size n=300, the standard error is of 0.027.
Samples of 400:
For samples of size n=400, the standard error is of 0.024.
Ответ:
i dont know..
explanation: im du-*mb as h-eII