Calculating Margin Of Error From Standard Error
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Calculating Margin In Excel
content right to your inbox. Easy! Your email Submit RELATED ARTICLES How calculating retail margin to Calculate the Margin of Error for a Sample… Statistics Essentials For Dummies Statistics For Dummies, 2nd Edition calculating profit margin SPSS Statistics for Dummies, 3rd Edition Statistics II for Dummies Load more EducationMathStatisticsHow to Calculate the Margin of Error for a Sample Mean How to Calculate the Margin of http://stattrek.com/estimation/margin-of-error.aspx Error for a Sample Mean Related Book Statistics For Dummies, 2nd Edition By Deborah J. Rumsey When a research question asks you to find a statistical sample mean (or average), you need to report a margin of error, or MOE, for the sample mean. The general formula for the margin of error for the sample mean (assuming a http://www.dummies.com/how-to/content/how-to-calculate-the-margin-of-error-for-a-sample-.html certain condition is met -- see below) is is the population standard deviation, n is the sample size, and z* is the appropriate z*-value for your desired level of confidence (which you can find in the following table). z*-Values for Selected (Percentage) Confidence Levels Percentage Confidence z*-Value 80 1.28 90 1.645 95 1.96 98 2.33 99 2.58 Note that these values are taken from the standard normal (Z-) distribution. The area between each z* value and the negative of that z* value is the confidence percentage (approximately). For example, the area between z*=1.28 and z=-1.28 is approximately 0.80. This chart can be expanded to other confidence percentages as well. The chart shows only the confidence percentages most commonly used. Here are the steps for calculating the margin of error for a sample mean: Find the population standard deviation and the sample size, n. The population standard deviation, will be given in the problem. Divide the population standard deviation by the square root of the sample size. gives you the standard error. Multiply by t
engineering, see Tolerance (engineering). For the eponymous movie, see Margin for error (film). The top portion charts probability density against actual https://en.wikipedia.org/wiki/Margin_of_error percentage, showing the relative probability that the actual percentage is realised, based on the sampled percentage. In the bottom portion, each line segment shows the 95% confidence interval http://stats.stackexchange.com/questions/15981/what-is-the-difference-between-margin-of-error-and-standard-error of a sampling (with the margin of error on the left, and unbiased samples on the right). Note the greater the unbiased samples, the smaller the margin of margin of error. The margin of error is a statistic expressing the amount of random sampling error in a survey's results. It asserts a likelihood (not a certainty) that the result from a sample is close to the number one would get if the whole population had been queried. The likelihood of a result being "within the margin margin of error of error" is itself a probability, commonly 95%, though other values are sometimes used. The larger the margin of error, the less confidence one should have that the poll's reported results are close to the true figures; that is, the figures for the whole population. Margin of error applies whenever a population is incompletely sampled. Margin of error is often used in non-survey contexts to indicate observational error in reporting measured quantities. In astronomy, for example, the convention is to report the margin of error as, for example, 4.2421(16) light-years (the distance to Proxima Centauri), with the number in parentheses indicating the expected range of values in the matching digits preceding; in this case, 4.2421(16) is equivalent to 4.2421 ± 0.0016.[1] The latter notation, with the "±", is more commonly seen in most other science and engineering fields. Contents 1 Explanation 2 Concept 2.1 Basic concept 2.2 Calculations assuming random sampling 2.3 Definition 2.4 Different confidence levels 2.5 Maximum and specific margins of error 2.6 Effec
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 company Business Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, 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 rise to the top What is the difference between “margin of error” and “standard error”? up vote 9 down vote favorite 4 Is "margin of error" the same as "standard error"? A (simple) example to illustrate the difference would be great! definition share|improve this question edited Sep 23 '11 at 18:04 whuber♦ 145k17281540 asked Sep 23 '11 at 17:06 Adhesh Josh 91283356 add a comment| 3 Answers 3 active oldest votes up vote 13 down vote accepted Short answer: they differ by a quantile of the reference (usually, the standard normal) distribution. Long answer: you are estimating a certain population parameter (say, proportion of people with red hair; it may be something far more complicated, from say a logistic regression parameter to the 75th percentile of the gain in achievement scores to whatever). You collect your data, you run your estimation procedure, and the very first thing you look at is the point estimate, the quantity that approximates what you want to learn about your population (the sample proportion of redheads is 7%). Since this is a sample statistic, it is a random variable. As a random variable, it has a (sampling) distribution that can be characterized by mean, variance, distribution function, etc. While the point estimate is your best guess regarding the population parameter, the standard error is your best guess regarding the standard deviation of your estimator (or, in some cases, the square root of the mean squared error, MSE = bias$^2$ + variance). For a sample of size $n=1000$, the standard error of your proportion estimate is $\sqrt{0.07\cdot0.93/1000}$ $=0.0081$. The margin of error is the half-width of the associated confidence interval, so for the 95% confidence level, you would have $z_{0.975}=1.96$ resulting in a margin of error $0.0081\cdot1.96=0.0158$. share|improve this answer edited Sep 23 '11 at 21:24 whuber♦ 145k17281540 answered Sep 23 '11 at 18:21 StasK 21.4k47102 add a comment| up vote 2 down vote