# confidence interval for proportion in excel

## confidence interval for proportion in excel

You need to use .05 instead of .05/2 = .025 I see two problems with the formula =CONFIDENCE(0.025, 0.01505, 1100) Confidence Interval for a Mean. Confidence Interval for Population Proportion in Excel. π = 0.55. Would you review the standard deviation calculation for Example 4? Apologies for overlooking your comment from a long time ago. The common proportion pi is calculated with denominator 200 = n_1 + n_2. what is the formula for Standard deviation over 1100?in Excel, please, Sorry, but I don’t understand your question. I’m not sure why this formula is incorrect and doesn’t return the same value as your calculation since they should both be equivalent and I’m not sure how I set up the CONFIDENCE equation incorrectly. Hi Required fields are marked *, Everything you need to perform real statistical analysis using Excel .. … … .. © Real Statistics 2020. It is fair to say, formally, that each of the 600 people asked : X1, X2,..,X600 ,is a proportionally-distributed random variable with mean p and variance p(1-p)/n so that =CONFIDENCE(alpha,standard_dev,size) The CONFIDENCE function uses the following arguments: 1. I want to conduct a power analysis in order to determine the sample size to compare two differents proportions. Charles, This a two sample hypothesis test. Thanks for catching this error. The manager (collections) of the bank feels that the proportion of the number of such credit card holders in the city – X is not different from the proportion of the number of such credit card holders in the city – Y. to test his intuition, a sample of 200 credit card holders is taken from the city – X and it is found that 160 of them are settling their excess withdrawal amount in – time without attracting interest. To illustrate the CONFIDENCE function, create a blank Excel worksheet, copy the following table, and then select cell A1 in your blank Excel worksheet. The manager (collections) of the bank feels that the proportion of the number of such credit card holders in the city – X is not different from the proportion of the number of such credit card holders in the city – Y. to test his intuition, a sample of 200 credit card holders is taken from the city – X and it is found that 160 of them are settling their excess withdrawal amount in – time without attracting interest. The number of credit card holders of a bank in two different cities (city – X and city – Y) settling their excess withdrawal amounts in time without attracting interest follows binomial distribution. Sorry but I don’t understand what X1, X2,..,X600 and p are. I show how to estimate the effect size using this approach on the website. I have belatedly corrected this error. Clealy state the null and alternative hypothesis.perfom on a 99% level of confidence, Hello Nikita, By serching a little bit, I found that we could use Fisher’s exact test, but i dont know how to conduct a power analysis for this test. Hi Sun, I have now corrected the error on the webpage. City X 30% customers settles their excess withdrawal in time (50/180 = 0.3) The observed value of x – y is .80 – .30 =.50, and so we have (two-tail test): Question for CASE STUDY 1 I think it would be difficult for me to respect the condition of [ ni πi ≥ 5 and ni (1 –πi) ≥ 5 ] since the πi is pretty close to 0 (in the order of 0.00008) . City Y = 180 credit card holders, 50 customers excess withdrawal in time, City X 80% customers settles their excess withdrawal in time (160/200 = 0.8) 2. Charles, Hakan just brought up the same issue. http://www.biostathandbook.com/fishers.html Charles. I will take a look at these references. q. Could you recommend a book or a reference that explain the calculation of the power analysis for different test. Thanks for bringing this to my attention and sorry that I didn’t see it earlier. Charles. Charles, Your email address will not be published. Another approach is to use Monte Carlo simulation.

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