Confidence Interval Calculation for Sample Mean: 95% Confidence Level

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In summary, the individual needs to determine a value v in order to have 95% confidence that the average is v or less. They can calculate a 90% confidence interval using the given data, with the upper bound of this interval being the value v they are looking for. The formula for the confidence interval is B% CI = [x-(1.645*s)/sqrt(n), x+(1.645*s)/sqrt(n)] and the logic used to determine v is valid.
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Homework Statement



I know the sample size n, the observed sample mean x, and the observed sample standard deviation s. I need to determine a value v such that I'm 95% confident that the average is v or less.

The Attempt at a Solution



If I calculate the 95% confidence interval, then I know that 95% of the resulting intervals will contain the true mean. Does the upper bound of the 95% confidence interval also tell me that this mean will be less than or equal to the upper bound with 95% confidence? Am I thinking about this the wrong way? Thanks
 
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I think the answer is to construct the 90% confidence interval using the data given. Because this interval will be centered on the observed sample mean x, only 5% of averages will be above the upper bound of this interval. Therefore, I can be 95% confident that the upper bound is the value v that I'm looking for.

B% CI = [x-(1.645*s)/sqrt(n), x+(1.645*s)/sqrt(n)]

So, v = x+(1.645*s)/sqrt(n).

Does that logic work?
 

1. What is a confidence interval?

A confidence interval is a range of values that is likely to contain the true population parameter with a certain degree of confidence. It is calculated from a sample and is used to estimate the true population parameter.

2. How is a 95% confidence level determined?

A 95% confidence level means that if we were to take multiple samples from the same population and calculate a confidence interval for each sample, then 95% of those intervals would contain the true population parameter. It is a commonly used confidence level in statistical analysis.

3. What is the formula for calculating a confidence interval for sample mean?

The formula for calculating a confidence interval for sample mean is: sample mean ± (critical value * standard error of the mean), where the critical value is determined based on the desired confidence level and the standard error of the mean is calculated from the sample data.

4. How do I interpret a confidence interval?

A confidence interval can be interpreted as a range of values that is likely to contain the true population parameter with a certain degree of confidence. For example, if a 95% confidence interval for the population mean is 50 to 60, we can say with 95% confidence that the true population mean falls within this range.

5. Why is it important to calculate a confidence interval?

Calculating a confidence interval allows us to estimate the true population parameter with a certain level of confidence. This is important because we can never know the exact value of the population parameter, and using a confidence interval gives us a range of values that is likely to contain the true parameter. It also helps us to assess the precision and reliability of our sample data.

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