Confidence Interval for the Mean

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=1.96sn√
E
=
1
.
96
s
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=1.96pˆ(1−pˆ)n‾‾‾‾‾‾√
E
=
1
.
96
p
^
(
1
-
p
^
)
n
where pˆ
p
^
is the sample proportion and n is the sample size.
Use the confidence interval formula above, and the correct formula for means, to calculate the 95% confidence interval for any population mean of your choice. Tell what quantitative variable you chose. Then, write down (invent) the sample size (be sure it is 30 or above), the sample mean, and the sample standard deviation. Then, calculate the confidence interval. Remember, you are inventing all the values, so no two posts should look the same.