One approach is to take a relatively small group of people (a sample) and find their average height. In practice, however, it's impossible from a time, money, and technical point of view, so we need to estimate such a value. Ideally, we should measure everybody, one by one, and, eventually, we would get a precise, well-defined number. Such measures usually form a normal distribution for large populations. Let's say we have a task to find the average height of adults within a country.
#HOW TO CALCULATE STANDARD ERROR IN STATA HOW TO#
Great! So then, why do we want to know how to find the standard error? Typically, if someone wants to know how to calculate standard error, it's the standard error of the mean, or SEM for short. In statistics, we can estimate the standard error of any parameter - a mean, a proportion, a difference of means, and many many more. So you see, you get a correction term using differences between the group means and the overall mean.To learn how to find a standard error, we first need to ask ourselves standard error of what? (with standard definitions of all the terms).
Then the correct method is to add a term depending on the differences between the means. But the calculations might be already done and reported, and you do not have access to the individual data points. The simplest thing is to do as first above, the group the 6 observations in one group. You might have three groups in the data, but your model is that the (theoretical) means and variances are the same. Then you can use the pooled standard deviation, which is the square root of the pooled sample variance: In this case, the number of observations in each group are equal, so all gets the same weight: > sqrt(mean(c(var(x1),var(x2),var(x3))))Ĭompare this to the three individual standard deviations: > c(sd(x1),sd(x2),sd(x3)) On the other hand, it seems like your assumption is that the means can be different, but the standard deviations (the theoretical parameter $\sigma$) are the same. That gives (using R, much better than excel, and free.): > x1 If you think all your data have a common mean, then you can just treat them as one sample with $n=n_1+n_2+n_3=2+2+2=6$ observations. Your question is unclear, because you did not state an statistical model, so we do not really know what are reasonable assumptions for your data. But this seems to not take into account the error found in the numbers I am averaging. The mean is easy: 1.09 I can also calculate the standard deviation for that calculation: 0.05.
But then I want to know the mean and standard deviation of the total. Using Excel, I quickly calculate means and standard deviations for each (A: mean 1.125, stdev 0.0353. Each sample is measured twice: for instance, A is 1.10 and 1.15, B is 1.02 and 1.05, and C is 1.11 and 1.09. I have three samples (which are supposedly identical), called A, B, and C. However, this feels like it underestimates the deviation, as we have not factored in the uncertainty in the mean of each. However, I can then calculate the mean of the three samples together, and a standard deviation for this mean. So, for each sample, I can calculate a mean and a standard deviation. For example, I have three samples, each of which I take two measurements of. I have essentially a propagation-of-error problem I run into frequently with my scientific data.
0 kommentar(er)
