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Are there different assumptions for the mean of the sum?

Say I have a sum of random variables, and I want to make sure that there is a guarantee that the mean of the sum is within some margin from the sum of the means. Is there some way to do this?
The problem I am trying to solve is adding a constant, and ensuring that the errors from the constant are within some margin from the constant.
Perhaps this is the wrong idea, but intuitively, the random variables need to be sufficiently spread out so that the mean is within the given margin.

A:

If you write $X_i$ for the $i$th summand and $X = \sum_{i=1}^n X_i$,
then the standard deviation of $X$ is
$$\sqrt{\operatorname{Var}(X)} = \sqrt{\sum_{i=1}^n \operatorname{Var}(X_i)}$$
and the mean deviation is
$$\frac{|X – \mu|}{\sigma}.$$
Any linear combination of the $X_i$ will have mean equal to $\mu$ and variance equal to the average of the variances, so the relative mean deviation will be the same.
If $\mu + c$ is within a given margin $\delta$ of the sum,
then the average variance will be within a margin $2\delta$ of the average of the variances, so the relative deviation will be within \$2\delta/

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