Bell Curve Chart and Transforming Data for Normality
Bell Curve Chart and Transforming Data for Normality
Has any one coded any bell curve formula or messed around with Transforming Data for Normality on a bell curve chart?
WARNING: I dont know what I am doing! I just put stuff here and there and sometimes like magic it works. So please improve on my code and post your changes so I can learn more. TIA
Re: Bell Curve Chart and Transforming Data for Normality
You would first have to assume the distribution was in fact Gaussian.
I don't know of any way to determine that other than calculating skew and kurtosis that = 0. (or Mode=1 [or 0?])
As you mentioned Bell Curve, that means Gaussian.
If Gaussian isn't a real issue then don't worry about it.
you might just mean "Bell Curve" as the "distribution".
You can normalize and denormalize your data per channel by using these formulas for amplitude and offset:
Amp(i) = (UpperBound - LowerBound) / (Max(i) - Min(i))
Off(i) = UpperBound - Amp(i) * Max(i)
where Max(i) and Min(i) are the maximum and minimum values found within channel i, and UpperBound and LowerBound are the values of the desired output data range.
Normalize the data using the following formula:
Data(i) = Amp(i) * Data(i) + Off(i)
NormalizedData=(Amp*RawData)+Offset
Formula to denormalize the data:
Data(i) = (Data(i) - Off(i)) / Amp(i)
I don't know of any way to determine that other than calculating skew and kurtosis that = 0. (or Mode=1 [or 0?])
As you mentioned Bell Curve, that means Gaussian.
If Gaussian isn't a real issue then don't worry about it.
you might just mean "Bell Curve" as the "distribution".
You can normalize and denormalize your data per channel by using these formulas for amplitude and offset:
Amp(i) = (UpperBound - LowerBound) / (Max(i) - Min(i))
Off(i) = UpperBound - Amp(i) * Max(i)
where Max(i) and Min(i) are the maximum and minimum values found within channel i, and UpperBound and LowerBound are the values of the desired output data range.
Normalize the data using the following formula:
Data(i) = Amp(i) * Data(i) + Off(i)
NormalizedData=(Amp*RawData)+Offset
Formula to denormalize the data:
Data(i) = (Data(i) - Off(i)) / Amp(i)