Curve fitting and normalization

[#1]

I have got a data set which is positively skewed and therefore need to normalize it. I have got a wave form line with age and challenging behaviours present in Individuals with intellectual disabilities. I am totally new to JMP and advanced statistical procedures and this is a big step in requirement of my PhD research. Any step by step guidelines or videos in scale development, curve fitting, normalizing data...Would greatly appreciate any help.

[#2]

Hello Again,

After Johnson curve fitting, I transformed the scores to a scale (m=15, sd=3), which is a bench mark for these kinds of tests. I have sent a section of my data here. The first col -raw score (0-27), the second col-scale score (1-24) and the third is, I fitted the second to kernel density estimation in excel, and got the third col, which is adjusted scaled score. The problem is, this adjusted scaled score should have mean 15, SD 3.  I do not know how to use kernel smoother in JMP. I have got five subscales with three age group norms, and am progressing halfway through. So far none of them fitted to mean-15, SD-3. it ranges from 15-19 but SD is ranging between 3 or 5. I am time pressed and have to figure out a way. I do not know if am right, I tried to use the third col.. into random number generation in excel with maximum value 24.. calculated m and sd of them to spot the right one. But its exhaustive to do that. I am not confident to explore non parametric statistics here but there has been evidence for kernel smoother to fit post norms smoothing. Would really appreciate any advice. Thanks.

[#3]

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 After Johnson curve fitting, I transformed the scores to a scale (m=15, sd=3), which is a bench mark for these kinds of tests. I have sent a section of my data here. The first col -raw score (0-27), the second col-scale score (1-24) and the third is, I fitted the second to kernel density estimation in excel, and got the third col, which is adjusted scaled score. The problem is, this adjusted scaled score should have mean 15, SD 3.

You transformed the scale to {m=15, sd=3}. JMP would try to center (m=0) and scale (sd=0) because that is the usual practice. For example, predictors centered and scaled this way meet regression assumptions better. It is not designed for some arbitrary transformaton. You can always multiply by a constant to achieve the sd that you want. You can always add a constant to achieve the mean that you want. Scale before you translate.

[#4]

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I do not know how to use kernel smoother in JMP.

What part of the JMP Help or guides (Help > Books > Basic Analysis: Distribution) don't you understand? Glad to help here.

[#5]

Thank you for the mail. Let me read "help-books" and explore the data with kernel smoother. I lack understanding of the bandwidth and what it does to distribution. Thanks much