Confidence interval, proportions

Using this formula, you should get a variance of 0.0008, which is the correct answer. In summary, the conversation discusses the calculation of a 99% confidence interval for the difference between the proportion of women and men who answered "yes" in a survey. The book provides an answer of (-0.091;0.075), but the person is struggling to get the same answer using their calculations. Their mistake is in calculating the variance of the difference between proportions.
  • #1
pinto89a
5
0
I a survey, out of 530 women and 509 men, 10.4% of the women and 21.6% of the men answered yes to a question.

Calculate a 99% CI for the difference between two times the population proportion women and the population proportion men who answered "yes".

Answer from book: (-0.091;0.075)

What I did was
p hat 0 = (530 * 0.208 + 509 * 0.216)/ (509 + 530)
and then find
s^2 (p hat x - p hat y) = P hat 0 * (1- p hat 0) * (1/509 + 1/530)
and finally -0008 +- the square root of that

But the answer I get is wrong and I can't see where I made a mistake. Any help please?
 
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  • #2
Your mistake is in calculating the variance of the difference between proportions. You have calculated the variance of a single proportion, but not the difference between two proportions. To calculate the variance of the difference between two proportions, you need to use the formula: Var(p1-p2) = p1(1-p1)/n1 + p2(1-p2)/n2 Where n1 and n2 are the sample sizes of the two groups. In your case, n1 = 530 and n2 = 509.
 

1. What is a confidence interval?

A confidence interval is a range of values that is likely to include the true value of a population parameter, such as a proportion. It is calculated from a sample and is used to estimate the true value of the population parameter with a certain level of confidence.

2. How is a confidence interval calculated for proportions?

A confidence interval for a proportion is calculated using the formula p ± z*√(p(1-p)/n), where p is the sample proportion, z is the z-score corresponding to the desired confidence level (e.g. 1.96 for a 95% confidence interval), and n is the sample size.

3. What is the significance of the confidence level in a confidence interval?

The confidence level represents the probability that the true value of the population parameter falls within the calculated confidence interval. For example, a 95% confidence interval means that if we were to take 100 different samples and calculate a confidence interval for each one, 95 of those intervals would contain the true value of the population parameter.

4. How does sample size affect the width of a confidence interval?

The larger the sample size, the smaller the width of the confidence interval. This is because as the sample size increases, the standard error (the measure of uncertainty in the sample proportion) decreases, allowing for a more precise estimate of the population proportion.

5. Can a confidence interval be used to make a definitive statement about the population parameter?

No, a confidence interval is an estimate and not a definitive statement about the population parameter. It is subject to sampling error and therefore cannot provide an exact value for the population parameter. However, it can provide a range of values within which the true value is likely to fall with a certain level of confidence.

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