Hi:
Recently, I want to perform a transformation on my data to make it more
normal, meanwhile the order statistics is unchanged. So I decided to use a
box-cox transformation.
below is the qq-plot of the original data
http://n4.nabble.com/file/n1011015/start%2Bvalue%2Bproblem%2B02.jpeg
Note that the min of my data is -1099, so I add a fix value 1200 to the
original sample.
I choose the "box.cox.powers" function in package 'car'. Here is the result:
> box.cox.powers(na.exclude(c888.dl.ma080+1200))
Box-Cox Transformation to Normality
Est.Power Std.Err. Wald(Power=0) Wald(Power=1)
0.9526 0.0237 40.2638 -2.0036
L.R. test, power = 0: 2014.192 df = 1 p = 0
L.R. test, power = 1: 3.9807 df = 1 p = 0.046
Then I compared the result with original data, and it really confused me:
http://n4.nabble.com/file/n1011015/start%2Bvalue%2Bproblem.jpeg
The left is my original data sample, you can see that it is symetric and the
mean is close to 0. It just that the spread is large (there are outliers).
The right is the transformed data, and the distribution is obviously no
normal.
Can anyone explain that to me?
Thank you in advanced.
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Saji Ren
from Shanghai China
GoldenHeart Investment Group
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