Calculating Confidence Interval

Sample statistics, such as the sample mean or the sample proportion, can be used to estimate a population parameter (such as the population mean or the population proportion). For example, you can estimate the true mean weight of all full-term newborn babies in the entire world by collecting a sample and using that sample to generate a 95% confidence interval.
Because the sample is a relatively small portion of the entire population, errors will have to be considered. Using a sample to create a range or interval of values that estimates a population value is called a “confidence interval.”
The general “confidence interval” formula is:
(sample statistic – E) < population parameter < (sample statistic + E)
To calculate a confidence interval, the margin of error (E) must first be calculated.
The margin of error, E, for means is: E=1.96s/√n, where s is the sample standard deviation and n is the sample size.
The margin of error, E, for a 95% confidence interval for proportions is: E=1.96√((p┴^ (1-p┴^))/n), where p┴^ is the sample proportion and n is the sample size.
Option B:
Use the confidence interval formula above, and the correct formula for proportion, to calculate the 95% confidence interval for any population proportion of your choice. Tell what qualitative variable you chose. Then, write down (invent) the sample size (be sure it is 30 or above) and the sample proportion. Then, calculate the confidence interval. Remember, you are inventing all the values, so no two posts should look the same.