What is the Approximate Confidence Interval for p?

In summary, the problem is asking to construct a confidence interval for a normal distribution with a range from 0 to 1. The solution involves finding the value of alpha and beta, where alpha is the area under the curve from L to R and beta is the area under the curve from -infinity to L. By setting R=1 and using the fact that the distribution is symmetric, the approximate confidence interval is found to be 100(1-alpha/2)%.
  • #1
Gekko
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Homework Statement



I re-wrote a previous thread just to make it clearer:

Construct a normal symmetric 100(1-alpha)% confidence interval [L,R] for p then show that [L,1] is then an approximate 100(1-alpha/2)% confidence interval for p

The normal distribution ranges from 0 to 1

The Attempt at a Solution



(1-alpha) = integral from L to R of e^-u^2 du. This means alpha = 1-integral from L to R of e^-u^2 du = integral from -inf to L of e^-u^2 + integral from R to inf of e^-u^2

When R=1, integral from L to inf of e-u^2 du = (1-beta). beta = 1- integral from -inf to L of e^-u^2 du. Since symmetric, beta = alpha /2
 
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  • #2
. Therefore, integral from -inf to L of e^-u^2 du = 1- alpha/2. This is the approximate 100(1-alpha/2)% confidence interval for p.
 

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 level of confidence. It is calculated from a sample of data and is used to estimate the true value of a population parameter.

2. How is a confidence interval calculated?

A confidence interval is calculated using the sample mean, sample standard deviation, and the desired level of confidence. The formula for calculating a confidence interval is: CI = x̅ ± z*(s/√n), where x̅ is the sample mean, s is the sample standard deviation, n is the sample size, and z is the z-score associated with the desired level of confidence.

3. What does the confidence level represent?

The confidence level represents the probability that the true population parameter falls within the calculated confidence interval. For example, a 95% confidence level means that there is a 95% chance that the true population parameter falls within the calculated confidence interval.

4. What is the relationship between sample size and confidence interval width?

The larger the sample size, the narrower the confidence interval will be. This is because a larger sample size provides more accurate estimates of the population parameters, resulting in a smaller margin of error.

5. How is a confidence interval used in hypothesis testing?

In hypothesis testing, a confidence interval is used to determine whether the null hypothesis should be rejected or not. If the hypothesized value falls within the confidence interval, then the null hypothesis cannot be rejected. However, if the hypothesized value falls outside the confidence interval, then the null hypothesis can be rejected.

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