This is not the best form of analysis a better approach would be to use the time covariate in a regression model.
![how to calculate standard error mean how to calculate standard error mean](https://www.codingem.com/wp-content/uploads/2021/11/image-2.png)
To see how this works more specifically, let $X_1.,X_n \sim \text_\mu(0.95) = \Big = \Big = \Big.$$Īs noted, this analysis ignores the time data, and simply treats all the values as a single IID sample, so it is important to remember that this confidence interval is contingent on that treatment of the data (which seems to be what you are after). In this case, the estimated standard error will generally be much smaller than the sample standard deviation of the original data points, since the mean estimator is less variable than the data itself. The t-quantile can be looked up for the level of confidence when the total sample size (n) and the number of. If we assume that the scores are drawn iid (and I dont see why we wouldnt, since its different players of different teams), then the 378 observations of average points per player (score/2 for each row) allow you to characterize the distribution of the statistic youre trying to estimate (in your case the 'mean of means'). For each value, find the square of this distance.
![how to calculate standard error mean how to calculate standard error mean](https://i.stack.imgur.com/VNKx7.png)
When you take a sample of observations from a. The steps in calculating the standard deviation are as follows: For each value, find its distance to the mean. The standard error is the standard deviation of an estimator the SEM therefore arises when you are using the sample mean as an estimator of the true underlying population mean. The formula can be solved for the SE: CI upper m + tSE -> SE (CI upper -m)/t. Standard error of the mean tells you how accurate your estimate of the mean is likely to be.