Wikipedia
confidence intervals : In statistics a robust confidence interval is a robust modification of confidence intervals, meaning that one modifies the non-robust calculations of the confidence interval so that they are not badly affected by outlying or aberrant observations in a data-set. In the process of weighing 1000.. More from Wikipedia
confidence intervals : In Bayesian statistics, a credible interval is a posterior probability interval which is used for interval estimation in contrast to point estimation. Credible intervals are used for purposes similar to those of confidence intervals in frequentist statistics and an alternative terminology.. More from Wikipedia
The formula for the confidence interval for a variance is _________ .
The formula for the confidence interval for a variance is _________ . => IV. or II. or III. or I...
What is meant by 95% confidence interval of the mean?
What is meant by 95% confidence interval of the mean? => 95% of the specific sample means will fall within ± 1.96 standard errors of the population mean. or 95% of the specific sample means will fall within ± 2.58 standard errors of the population mean. or There is..
Statistics Confidence Interval Definition and formula
Amir H. Ghaseminejad shows the meaning of Confidence Interval with a simple example
Question : Confidence Intervals?
I've been using similar questions to try and answer the one below but it keeps coming out a bit wrong. Any ideas?
"A hospital is concerned about errors made by the pharmacy in dispensing prescriptions. An audit of 200 randomly selected prescriptions revealed errors in 38 cases. Obtain an estimate of the error rate, together with an approximate 95% confidence interval for the true or "population" value."
Best answer gets 10 points!
Thanks.
Answer : The confidence interval is m - c < x < m+c, where m is the mean, ie the error rate which I'm sure you can calculate for yourself, and c is given by c = alpha * standard deviation / square root of number of cases For a 95% confidence interval, use alpha = 1.96. Because a case can only be "correct" or "error", this is a binomial distribution with parameter p which is equal to the error rate in this example. The standard deviation of the binomial distribution is equal to p(1-p). If you substitute this into the calculation for the confidence interval, put your numbers in, this should give you your answer... More from Yahoo Answers
Answer : The confidence interval is m - c < x < m+c, where m is the mean, ie the error rate which I'm sure you can calculate for yourself, and c is given by c = alpha * standard deviation / square root of number of cases For a 95% confidence interval, use alpha = 1.96. Because a case can only be "correct" or "error", this is a binomial distribution with parameter p which is equal to the error rate in this example. The standard deviation of the binomial distribution is equal to p(1-p). If you substitute this into the calculation for the confidence interval, put your numbers in, this should give you your answer... More from Yahoo Answers
Question : Hello. So the question is there are 200 people who are all making 95% confidence intervals (using the same data and etc) and determining if X falls within that confidence interval.
1) How many people are expected to find that X falls within their interval.
Is it 200*0.95 = 190?
2) What is the probability that all 200 students get X in their interval?
I was thinking that it is 0.95^200 but that can't be right.
Any help would be greatly appreciated.
Answer : 1) If you are to run a large number of confidence intervals, each based on a separate estimate for the mean (with confidence level (1 - alpha)), then yes, the expected number of confidence intervals containing the true mean would be n * (1 - alpha), or 200 * 0.95 like you said. In general, this means that any future confidence interval (the sample has not yet been measured or tabulated) has a 95% chance of containing the true mean. It DOES NOT mean that an interval that has already been run has a 95% chance. Probability only applies to events which haven't happened yet. 2) How to find the probability that all 200 students will get the true mean in their interval is a binomial distribution question. Sample size is n, probability of success, p, is 0.95. So, the probability of 200 independent successes would be (0.95) ^ (200), or 0.00003505, or 0.003505%. Even the most likely outcome, exactly 190 successes and 10 failures, has the relatively small probability of .1284, or 12.84%. .... More from Yahoo Answers
Answer : 1) If you are to run a large number of confidence intervals, each based on a separate estimate for the mean (with confidence level (1 - alpha)), then yes, the expected number of confidence intervals containing the true mean would be n * (1 - alpha), or 200 * 0.95 like you said. In general, this means that any future confidence interval (the sample has not yet been measured or tabulated) has a 95% chance of containing the true mean. It DOES NOT mean that an interval that has already been run has a 95% chance. Probability only applies to events which haven't happened yet. 2) How to find the probability that all 200 students will get the true mean in their interval is a binomial distribution question. Sample size is n, probability of success, p, is 0.95. So, the probability of 200 independent successes would be (0.95) ^ (200), or 0.00003505, or 0.003505%. Even the most likely outcome, exactly 190 successes and 10 failures, has the relatively small probability of .1284, or 12.84%. .... More from Yahoo Answers
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