Calculator Standard Error Proportion
Contents |
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow standard error of proportion formula the company Business Learn more about hiring developers or posting ads with us Cross
Square Root Calculator
Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, confidence interval calculator machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and
Margin Of Error Calculator
rise to the top How can I calculate the standard error of a proportion? up vote 1 down vote favorite 2 I made a comparison of hatch success between 2 populations of birds using R's prop.test() function: prop.test(c(#hatched_site1, #hatched_site2),c(#laid_site1, #laid_site2)) It gave me the proportions of each site as part of the summary. How can I calculate the standard error for each proportion? r standard-deviation proportion share|improve this question edited May 20 '11 sample proportion formula at 11:06 Bernd Weiss 5,7042138 asked May 20 '11 at 0:39 Mog 4382820 1 Do you mean the standard error for each proportion? If this is what you are looking for, then this webpage might be of interest for you: Interval Estimate of Population Proportion –Bernd Weiss May 20 '11 at 1:11 That'll do! I can calculate SD from SEM. I suppose I could've done the same to calculate SEM from the 95% confidence interval provided by prop.test too, but this is better. Thanks @Bernd! –Mog May 20 '11 at 2:22 1 Nooo! As @Bernd noted, the proportion does not have a standard deviation. Or more precisely, it does, but it is called the standard error. Standard deviation refers to the variability of the original 0-1 variable. –Aniko May 20 '11 at 2:55 Oh! Wow, thanks for the clarification @Aniko...that wouldn't have been good to report. Ok, I'll stick with the standard error. Thanks again! –Mog May 20 '11 at 3:43 1 Even more precisely, "standard error" of the proportion refers to the standard deviation of the distribution of the sample proportions from random samples of the particular sample size from the population of interest. –Thomas Levine May 20 '11 at 15:55 add a comment| active oldest votes Know
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the
Standard Error Of Proportion Definition
company Business Learn more about hiring developers or posting ads with us Cross Validated
Sample Proportion Probability Calculator
Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine sampling distribution of p hat calculator learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise http://stats.stackexchange.com/questions/11008/how-can-i-calculate-the-standard-error-of-a-proportion to the top How to calculate the standard error of a proportion using weighted data? up vote 3 down vote favorite I know the "textbook" estimate of the standard error of a proportion is $SE=\sqrt{\frac{p(1-p)}{n}}$, but does this hold up when the data are weighted? standard-error proportion weighted-data share|improve this question edited Jun 29 '15 at 20:14 whuber♦ 145k17281540 asked Jun 29 '15 at 17:38 simudice 303 This is the root of http://stats.stackexchange.com/questions/159204/how-to-calculate-the-standard-error-of-a-proportion-using-weighted-data the inverse of the Fisher information for a binomial distribution. The Fisher information is the variance of the expected value of the observed information. It is the standard deviation of the expected error. This expression should be valid for all binomial distributions. In practice, if the probability is quite close to one or to zero while you have few samples, the value given by the expression might have large error. Make sure your sample sizes are large enough. –EngrStudent Jun 29 '15 at 17:59 add a comment| 1 Answer 1 active oldest votes up vote 5 down vote accepted Yes, this formula generalizes in a natural way. Standardize the (positive) weights $\omega_i$ so they sum to unity. In a simple random sample $X_1, \ldots, X_n$ where each $X_i$ independently has a Bernoulli$(p)$ distribution and weight $\omega_i$, the weighted sample proportion is $$\bar X = \sum_{i=1}^n \omega_i X_i.$$ Since the $X_i$ are independent and each one has variance $\text{Var}(X_i) = p(1-p)$, the sampling variance of the proportion therefore is $$\text{Var}(\bar X) = \sum_{i=1}^n \text{Var}(\omega_i X_i) = p(1-p)\sum_{i=1}^n\omega_i^2.$$ The standard error of $\bar X$ is the square root of this quantity. Because we do not know $p(1-p)$, we have to estimate it. Although there are many possible estimators, a conventional one is to use $\hat p = \bar X$, the samp
test AP formulas FAQ AP study guides AP calculators Binomial Chi-square f Dist Hypergeometric Multinomial Negative binomial Normal Poisson t http://stattrek.com/estimation/confidence-interval-proportion.aspx?Tutorial=Stat Dist Random numbers Probability Bayes rule Combinations/permutations Factorial Event counter Wizard http://onlinestatbook.com/2/estimation/proportion_ci.html Graphing Scientific Financial Calculator books AP calculator review Statistics AP study guides Probability Survey sampling Excel Graphing calculators Book reviews Glossary AP practice exam Problems and solutions Formulas Notation Share with Friends Confidence Interval: Proportion (Large Sample) This lesson describes how standard error to construct a confidence interval for a sample proportion, p, when the sample size is large. Estimation Requirements The approach described in this lesson is valid whenever the following conditions are met: The sampling method is simple random sampling. The sample is sufficiently large. As a rule of thumb, a sample is considered standard error of "sufficiently large" if it includes at least 10 successes and 10 failures. Note the implications of the second condition. If the population proportion were close to 0.5, the sample size required to produce at least 10 successes and at least 10 failures would probably be close to 20. But if the population proportion were extreme (i.e., close to 0 or 1), a much larger sample would probably be needed to produce at least 10 successes and 10 failures. For example, imagine that the probability of success were 0.1, and the sample were selected using simple random sampling. In this situation, a sample size close to 100 might be needed to get 10 successes. The Variability of the Sample Proportion To construct a confidence interval for a sample proportion, we need to know the variability of the sample proportion. This means we need to know how to compute the standard deviation and/or the standard error of the sampling distr
on the Mean Learning Objectives Estimate the population proportion from sample proportions Apply the correction for continuity Compute a confidence interval A candidate in a two-person election commissions a poll to determine who is ahead. The pollster randomly chooses 500 registered voters and determines that 260 out of the 500 favor the candidate. In other words, 0.52 of the sample favors the candidate. Although this point estimate of the proportion is informative, it is important to also compute a confidence interval. The confidence interval is computed based on the mean and standard deviation of the sampling distribution of a proportion. The formulas for these two parameters are shown below: μp = π Since we do not know the population parameter π, we use the sample proportion p as an estimate. The estimated standard error of p is therefore We start by taking our statistic (p) and creating an interval that ranges (Z.95)(sp) in both directions, where Z.95 is the number of standard deviations extending from the mean of a normal distribution required to contain 0.95 of the area (see the section on the confidence interval for the mean). The value of Z.95 is computed with the normal calculator and is equal to 1.96. We then make a slight adjustment to correct for the fact that the distribution is discrete rather than continuous.
Normal Distribution Calculator sp is calculated as shown below: To correct for the fact that we are approximating a discrete distribution with a continuous distribution (the normal distribution), we subtract 0.5/N from the lower limit and add 0.5/N to the upper limit of the interval. Therefore the confidence interval is Lower limit: 0.52 - (1.96)(0.0223) - 0.001 = 0.475 Upper limit: 0.52 + (1.96)(0.0223) + 0.001 = 0.565 0.475 ≤ π ≤ 0.565 Since the interval extends 0.045 in both directions, the margin of error is 0.045. In terms of percent, between 47.5% and 56.5% of the voters favor the candidate and the margin of error is 4.5%. Keep in mind that the margin of error of 4.5% is the margin of error for the percen