Compute Estimated Standard Error Sample Mean
Contents |
to a normally distributed
Standard Error Of Sample Mean Distribution
sampling distribution whose overall mean is equal to the mean of the source standard error of sample mean excel 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 standard error of sample mean formula 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.
randomly standard deviation sample mean drawn from the same normally distributed source population, belongs to
Standard Error Of Sample Proportion
a normally distributed sampling distribution whose overall mean is equal to zero and whose standard deviation ("standard http://vassarstats.net/dist.html error") is equal to square.root[(sd2/na) + (sd2/nb)] where sd2 = the variance of the source population (i.e., the square of the standard deviation); na = the size of sample A; and nb = http://vassarstats.net/dist2.html the size of sample B. To calculate the standard error of any particular sampling distribution of sample-mean differences, enter the mean and standard deviation (sd) of the source population, along with the values of na andnb, and then click the "Calculate" button. -1sd mean +1sd <== sourcepopulation <== samplingdistribution standard error of sample-mean differences = ± sd of source population sd = ± size of sample A = size of sample B = Home Click this link only if you did not arrive here via the VassarStats main page. ©Richard Lowry 2001- All rights reserved.
of the mean of a set of numbers. Standard http://onlinestatbook.com/2/estimation/mean.html Error of the Mean The standard error of the mean is the standard deviation of standard error the sample mean estimate of a population mean. It is usually calculated by the sample estimate of the population standard deviation (sample standard deviation) divided by the square root of standard error of the sample size (assuming statistical independence of the values in the sample): Where: SEM = standard error of the mean s = sample standard deviation (see formula below) n = size (number of observations) of the sample The following is the sample standard deviation formula: Where: s = sample standard deviation x1, ..., xN = the sample data set x̄ = mean value of the sample data set N = size of the sample data set ©2016 Miniwebtool | Terms and Disclaimer | Privacy Policy | Contact Us
normal distribution calculator to find the value of z to use for a confidence interval Compute a confidence interval on the mean when σ is known Determine whether to use a t distribution or a normal distribution Compute a confidence interval on the mean when σ is estimated View Multimedia Version When you compute a confidence interval on the mean, you compute the mean of a sample in order to estimate the mean of the population. Clearly, if you already knew the population mean, there would be no need for a confidence interval. However, to explain how confidence intervals are constructed, we are going to work backwards and begin by assuming characteristics of the population. Then we will show how sample data can be used to construct a confidence interval. Assume that the weights of 10-year-old children are normally distributed with a mean of 90 and a standard deviation of 36. What is the sampling distribution of the mean for a sample size of 9? Recall from the section on the sampling distribution of the mean that the mean of the sampling distribution is μ and the standard error of the mean is For the present example, the sampling distribution of the mean has a mean of 90 and a standard deviation of 36/3 = 12. Note that the standard deviation of a sampling distribution is its standard error. Figure 1 shows this distribution. The shaded area represents the middle 95% of the distribution and stretches from 66.48 to 113.52. These limits were computed by adding and subtracting 1.96 standard deviations to/from the mean of 90 as follows: 90 - (1.96)(12) = 66.48 90 + (1.96)(12) = 113.52 The value of 1.96 is based on the fact that 95% of the area of a normal distribution is within 1.96 standard deviations of the mean; 12 is the standard error of the mean. Figure 1. The sampling distribution of the mean for N=9. The middle 95% of the distribution is shaded. Figure 1 shows that 95% of the means are no more than 23.52 units (1.96 standard deviations) from the mean of 90. Now consider the probability that a sample mean computed in a random sample is within 23.52 units of the population mean of 90. Since 95% of the distribution is within 23.52 of 90, the probability that the mean from any given sample will be within 23.52 of 90 is 0.95. This means that if we repeatedly compute the mean (M) from a sample, and create an interval ranging from M - 23.52 to M + 23.52, this interval will contain the population mean 95% of the time. In general, you compute the 95% confidence interval for the mean with the following formula: Lower limit = M - Z.95σM Upper limit = M + Z.95σM where Z.95 is the number o