# How to transform data to normality?

We have financial some data (500-1000 samples), which is not normally distributed (well known fact from the literature). I have some ideas to do parametric transformations of this data (using some other data) to produce "adjusted" series. My goal is to find a transformation that makes the series normally distributed (with mean 0 and std deviation 1). What is the most appropriate statistic and corresponding test to optimize my parameters and determine if the outcome can be considered normally distributed?

Please also point me to an implementation, ideally in C/C++ or java.

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Your transformation would need to be a bit weird in that it would need to pull the tails in -- preferably in a smooth way. But I'm not convinced of the advantage. What are you doing that you think you need a normal distribution? –  Patrick Burns Jan 1 '12 at 16:02
Your question leaves me unclear. You say you have financial data and refer to them as a "series". Are you doing time series analysis, and want a procedure and test to achieve stationarity? That's not quite the same as what you seem to be asking, and while you would need to get a stationary time series, as @PatrickBurns notes, it wouldn't necessarily need to be normally distributed. –  gung Jan 1 '12 at 22:09
Why not just use R? The shapiro.test function will do the work for you.