Distribution Standard Error
Contents |
proportion of samples that would fall between 0, 1, 2, and 3 standard deviations above and below the actual value. The standard error (SE) is the standard deviation of the sampling distribution of a statistic,[1] most commonly of the mean. The term
Distribution Standard Deviation
may also be used to refer to an estimate of that standard deviation, derived from distribution confidence interval a particular sample used to compute the estimate. For example, the sample mean is the usual estimator of a population mean. However, different distribution variance samples drawn from that same population would in general have different values of the sample mean, so there is a distribution of sampled means (with its own mean and variance). The standard error of the mean (SEM) (i.e.,
Distribution Median
of using the sample mean as a method of estimating the population mean) is the standard deviation of those sample means over all possible samples (of a given size) drawn from the population. Secondly, the standard error of the mean can refer to an estimate of that standard deviation, computed from the sample of data being analyzed at the time. In regression analysis, the term "standard error" is also used in the phrase standard error of the
Distribution Z Score
regression to mean the ordinary least squares estimate of the standard deviation of the underlying errors.[2][3] Contents 1 Introduction to the standard error 1.1 Standard error of the mean 1.1.1 Sampling from a distribution with a large standard deviation 1.1.2 Sampling from a distribution with a small standard deviation 1.1.3 Larger sample sizes give smaller standard errors 1.1.4 Using a sample to estimate the standard error 2 Standard error of the mean 3 Student approximation when σ value is unknown 4 Assumptions and usage 4.1 Standard error of mean versus standard deviation 5 Correction for finite population 6 Correction for correlation in the sample 7 Relative standard error 8 See also 9 References Introduction to the standard error[edit] The standard error is a quantitative measure of uncertainty. Consider the following scenarios. Scenario 1. For an upcoming national election, 2000 voters are chosen at random and asked if they will vote for candidate A or candidate B. Of the 2000 voters, 1040 (52%) state that they will vote for candidate A. The researchers report that candidate A is expected to receive 52% of the final vote, with a margin of error of 2%. In this scenario, the 2000 voters are a sample from all the actual voters. The sample proportion of 52% is an estimate of the true proportion who will vote for candidate A in the actual elect
Retirement Personal Finance Trading Q4 Special Report Small Business Back to School Reference Dictionary Term Of The Day Free Trade The unrestricted purchase and sale of goods and services between countries without ... Read More »
Standard Error Of Distribution Calculator
Basics Economics Basics Options Basics Exam Prep Series 7 Exam CFA Level 1 Series 65 Exam Simulator Stock Simulator poisson distribution standard error Trade with a starting balance of $100,000 and zero risk! FX Trader Trade the Forex market risk free using our free Forex trading simulator. Advisor Insights Newsletters Site Log https://en.wikipedia.org/wiki/Standard_error In Advisor Insights Log In Standard Error Loading the player... What is a 'Standard Error' A standard error is the standard deviation of the sampling distribution of a statistic. Standard error is a statistical term that measures the accuracy with which a sample represents a population. In statistics, a sample mean deviates from the actual mean of a population; this deviation is the standard error. BREAKING DOWN 'Standard Error' The term "standard error" is used to refer http://www.investopedia.com/terms/s/standard-error.asp to the standard deviation of various sample statistics such as the mean or median. For example, the "standard error of the mean" refers to the standard deviation of the distribution of sample means taken from a population. The smaller the standard error, the more representative the sample will be of the overall population.The standard error is also inversely proportional to the sample size; the larger the sample size, the smaller the standard error because the statistic will approach the actual value.The standard error is considered part of descriptive statistics. It represents the standard deviation of the mean within a dataset. This serves as a measure of variation for random variables, providing a measurement for the spread. The smaller the spread, the more accurate the dataset is said to be.Standard Error and Population SamplingWhen a population is sampled, the mean, or average, is generally calculated. The standard error can include the variation between the calculated mean of the population and once which is considered known, or accepted as accurate. This helps compensate for any incidental inaccuracies related the gathering of the sample.In cases where multiple samples are collected, the mean of each sample may vary slightly from the others, creating a spread among the variables. This spread is most often measured as the standard error, accounting for the differences between the means across the datasets.The more data points involved in the calculatio
to a normally distributed http://stats.stackexchange.com/questions/29641/standard-error-for-the-mean-of-a-sample-of-binomial-random-variables sampling distribution whose overall mean is equal to the mean of the source standard error population and whose standard deviation ("standard error") is equal to the standard deviation of the source population divided by the square root ofn. To calculate the standard error distribution standard error of any particular sampling distribution of sample means, enter the mean and standard deviation (sd) of the source population, along with the value ofn, and then click the "Calculate" button. -1sd mean +1sd <== sourcepopulation <== samplingdistribution standard error of sample means = ± parameters of source population mean = sd = ± sample size = Home Click this link only if you did not arrive here via the VassarStats main page. ©Richard Lowry 2001- All rights reserved.
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 Standard error for the mean of a sample of binomial random variables up vote 21 down vote favorite 8 Suppose I'm running an experiment that can have 2 outcomes, and I'm assuming that the underlying "true" distribution of the 2 outcomes is a binomial distribution with parameters $n$ and $p$: ${\rm Binomial}(n, p)$. I can compute the standard error, $SE_X = \frac{\sigma_X}{\sqrt{n}}$, from the form of the variance of ${\rm Binomial}(n, p)$: $$ \sigma^{2}_{X} = npq$$ where $q = 1-p$. So, $\sigma_X=\sqrt{npq}$. For the standard error I get: $SE_X=\sqrt{pq}$, but I've seen somewhere that $SE_X = \sqrt{\frac{pq}{n}}$. What did I do wrong? binomial standard-error share|improve this question edited Jun 1 '12 at 17:56 Macro 24.2k496130 asked Jun 1 '12 at 16:18 Frank 3561210 add a comment| 4 Answers 4 active oldest votes up vote 25 down vote accepted It seems like you're using $n$ twice in two different ways - both as the sample size and as the number of bernoulli trials that comprise the Binomial random variable; to eliminate any ambiguity, I'm going to use $k$ to refer to the latter. If you have $n$ independent samples from a ${\rm Binomial}(k,p)$ distribution, the variance of their sample mean is $$ {\rm var} \left( \frac{1}{n} \sum_{i=1}^{n} X_{i} \right) = \frac{1}{n^2} \sum_{i=1}^{n} {\rm var}( X_{i} ) = \frac{ n {\rm var}(X_{i}) }{ n^2 } = \frac{ {\rm var}(X_{i})}{n} = \frac{ k pq }{n} $$ where $q=1-p$ and $\overline{X}$ is the same mean. This follows since (1) ${\rm var}(cX) = c^2 {\rm var}(X)$, for any random variable, $X$, and any constant $c$. (2) the variance of a sum of independent random variables equals the sum of the variances. The standard error of $\overline{X}$is the square root of the variance: $\sqrt{\frac{ k pq }{n}}$. Therefore, When $k = n$, you get the formula you pointed