Confidence intervals how to find?

  • Thread starter semidevil
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In summary, researchers found that among the 169 women tested, 27 had BRCA 1 mutations. This means that there is a 5% chance that any woman in the population will have a BRCA 1 mutation.
  • #1
semidevil
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so if I want to find the 90% confidence interval..how do I do it?

all I know is that given 220 salads, 179 were contaminated.

i'm asked to find the 90% confidence interval, the true proportion of the contimatned salads.

so the formula is 100(1- a)% confidnece =[(y - z(a/2) * sigma/root(n)), ((y + z(a/2) * sigma/root(n)].

so my a is .9 right? since I am looking for 90%? so do I just do Z(.9/2) and look at the table?

what about n and sigma? n is 220, and what is sigma? what about the y's
 
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  • #2
Are you familiar with the binomial distribution? I think that's what you want to use to get your sigma (= standard deviation).
 
  • #3
semidevil said:
so if I want to find the 90% confidence interval..how do I do it?

all I know is that given 220 salads, 179 were contaminated.

i'm asked to find the 90% confidence interval, the true proportion of the contimatned salads.

so the formula is 100(1- a)% confidnece =[(y - z(a/2) * sigma/root(n)), ((y + z(a/2) * sigma/root(n)].

so my a is .9 right? since I am looking for 90%? so do I just do Z(.9/2) and look at the table?

what about n and sigma? n is 220, and what is sigma? what about the y's
Consider the sample of 220 salads to be 220 independent events having the Binomial Distribution. The proportion "p" of contaminated salads will then be Binomially Distributed:
{Observed Proportion} = p = (179/220) = (0.8136)
{Estimated Proportion Std Dev} = sqrt{p(1 - p)/N} = sqrt{(0.8136)(1 - 0.8136)/220) = (0.02626)

Because sample size is large, the Binomial Distr of "p" is approximated by the Normal Distr of "p" having the same Mean and Std Dev. For a 2-Tailed 90% (Normal Distr) Confidence Interval on the Population Proportion μ:
Prob{(-1.645) < Z < (+1.645)} = 0.90
Prob{(-1.645) < {(0.8136) - μ}/(0.02626) < (+1.645)} = 0.90
Prob{(0.8568) > μ > (0.7704)} = 0.90

90% Confidence Interval for Population Proportion μ is (0.7704, 0.8568)


~~
 
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  • #4
how did you go from Prob{(-1.645) < {(0.8136) - μ}/(0.02626) < (+1.645)} = 0.90 to Prob{(0.8568) > μ > (0.7704)} = 0.90 ?
 
  • #5
How can i work on this one? I have been stuck for 2 hours: BRCA 1 is a gene that has been linked to breast cancer. Researchers used DNA analysis to search for BRCA 1 mutations in 169 women with family histories of breast cancer. Of the 169 women tested, 27 has BRCA 1 mutations. Let p denote the probability that a woman with a family history of breast cancer will have a BRCA 1 mutation. Find a 95% confidence interval for p.
 

FAQ: Confidence intervals how to find?

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 used to estimate the true value of a population parameter based on a sample from the population.

2. How do you calculate a confidence interval?

The formula for a confidence interval is: point estimate ± margin of error. The point estimate is the sample statistic, such as the sample mean or proportion, and the margin of error is determined by the confidence level and the sample size.

3. What is the difference between a 90% and 95% confidence interval?

The confidence level represents the percentage of times that the true population parameter will be contained within the confidence interval. A 90% confidence interval means that there is a 90% chance that the interval contains the true population parameter, while a 95% confidence interval means there is a 95% chance.

4. How does sample size affect confidence intervals?

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

5. What factors may impact the accuracy of a confidence interval?

The accuracy of a confidence interval can be affected by various factors, such as the sample size, the variability of the population, the sampling method, and the chosen confidence level. It is important to carefully select these factors in order to obtain a reliable and accurate confidence interval.

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