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Thread: data transformation links please

  1. #1
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    data transformation links please




    Can anyone suggest a guide or link for more information on the transformation or non normally dirtibuted data?
    many thanks
    yoma

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    Here is a start: http://pareonline.net/getvn.asp?v=8&n=6. There are some refs at the end of that too. Tabachnick and Fidell is always a good source. There is plenty of this on the net though so give it a good google search and you should find your answers.

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    This site is a trusted favorate:


    http://udel.edu/~mcdonald/stattransform.html

    P
    The earth is round: P<0.05

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    Thanks for the links!
    depressingly i have 4 sets of data, all with different distributions:
    first one: lognormal
    second one:burr(4p)
    third one: log-logistic (3p)
    fourth :gamma (3p)

    has anyone got any advice for these specific distributions?
    cheers
    --yoma

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    Why are you interested in transforming the data? Its possible that you could use the data as-is.

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    my data is definitely not normally distributed and for a glm it does need to be.
    the only problem is i dont seem to be having much luck.
    to try and normalise the gamma distribution i have tried:
    square
    square root
    log

    but to no avail.
    this is the first time i have done data transformation and would appreciate any help
    cheers
    Yoma

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    I agree with Link. Whatever you're trying to do can probably be done with the knowledge you have. You seem to know quite a bit about the data. Are you positive that those are the distributions or are you just guessing?

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    100% positive.
    i have run the tests.
    basically i ran it through minitab which told me that my 4 sets of data were not normally distributed then i ran it through easyfit which told me which distributions fitted my data.
    have a look:
    http://s884.photobucket.com/albums/ac50/yoma819/
    cheers
    yoma

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    Ok but you still haven't told us what you're trying to do with the data. Also, just because you ran it through a program and it told you what it thinks the distribution is doesn't make you 100% positive that that is the true distribution. It's giving you it's best guess. I was asking if you knew that it came from some theoretical distribution (like you generated the data yourself or something). I mean I can generate some data from the gamma distribution and you'd could be pretty convinced that it came from a normal distribution if I didn't tell you what it really was...

    So what are you trying to do with this data? Why do you feel it needs to be normally distributed?

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    i am trying to fit it to a General linear model to see if there is a significant difference between wild type weight and 1a,and 1b and 1c between levels of two factors one with 7 levels and another with 2.
    basically what i was trying to find out how to do in this post:
    Code: 
    http://talkstats.com/showthread.php?t=13096
    the data is not generated but from an experiment (same experiment as in the post linked to above)
    i need the data to be normally distributed as this is an assumption of a glm!

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    Actually we don't care if the data is normally distributed. Just if the residuals are normally distributed.

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    how would i go about doing that?
    i have access to minitab, r and easyfit!
    why would i be only interested in the normality of my residuals?
    cheers
    yoma

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    Quote Originally Posted by yoma819 View Post
    my data is definitely not normally distributed and for a glm it does need to be.
    the only problem is i dont seem to be having much luck.
    to try and normalise the gamma distribution i have tried:
    square
    square root
    log

    but to no avail.
    this is the first time i have done data transformation and would appreciate any help
    cheers
    Yoma
    I remember a classmate of mine worrying about this once. I sat and listened while he explained it to the professor. From my memory of the conversation, you don't need to worry about the data being normally distributed for glm's. What you need to verify is that the residuals are normally distributed. Transforming the data isn't necessary if you're worried about that.

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    Quote Originally Posted by Dason View Post
    Actually we don't care if the data is normally distributed. Just if the residuals are normally distributed.
    LOL. Danggit Dason! Got there before I did!

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    ok
    I have looked over again and using a regression plot it does not look like they are normally distributed.

    HTML Code: 
    http://s884.photobucket.com/albums/ac50/yoma819/?action=view&current=minitabresidual.jpg
    1a, 1b and 1c are plotted against wt
    many thanks
    yoma

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