I am looking for a method to transform my dataset from its current mean and standard deviation to a target mean and a target standard deviation. Basically, I want to shrink/expand the dispersion and scale all numbers to a mean.
It doesn't work to do two separate linear transformations, one for standard deviation, and then one for mean. What method should I use?
Could the solution possibly be applied to an example where a point 1.02 in a dataset with SD .4 and a mean 0.88 is transformed when I adjust the mean of the dataset to 0.5 and the SD to 0.1667? What is the new value of the point?