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So we have arithmetic mean (am), geometric mean (gm) and harmonic mean (hm) It depends on what you are interested in. Their mathematical formulation is also well known along with their associated stereotypical examples (e.g., harmonic mea.
What does it imply for standard deviation being more than twice the mean One cannot generally say that the mean should or should not be used if we don't have a normal distribution Our data is timing data from event durations and so strictly positive
(sometimes very small negatives show up due to clock
The mean is the number that minimizes the sum of squared deviations Absolute mean deviation achieves point (1), and absolute median deviation achieves both points (1) and (3). After calculating the sum of absolute deviations or the square root of the sum of squared deviations, you average them to get the mean deviation and the standard deviation respectively The mean deviation is rarely used.
The above calculations also demonstrate that there is no general order between the mean of the means and the overall mean In other words, the hypotheses mean of means is always greater/lesser than or equal to overall mean are also invalid. Are these theoretical variances (moments of distributions), or sample variances If they are sample variances, what is the relation between the samples
Do they come from the same population
If yes, do you have available the size of each sample If the samples do not come from the same population, how do you justify averaging over the variances? 6 you can just use a standard confidence interval for the mean This is what the 'regression' command does and what the original poster is asking about
Also, for ols regression, r^2 is the squared correlation between the predicted and the observed values Estimate meanlog 6.0515 sdlog 0.3703 how to calculate the mean and sd of this distribution? The mean has a proper interpretation outside normal distributions, and it can have problems, such as its vulnerability to outliers (which in some applications is more of a problem than in others)
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