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Thread: data transformation links please

  1. #16
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    Quote Originally Posted by yoma819 View Post
    why would i be only interested in the normality of my residuals?
    The only reason we even care is because one of the assumptions we make when deriving the theory is that the errors are normally distributed. We don't care how the data itself is distributed because we say that once we adjust for our predictors the errors/residuals will be normally distributed.

    I guess one way to see why this is what we care about is consider we are comparing two groups.
    Code: 
    #data from first group
    y1 <- rnorm(100,5)
    #data from second group
    y2 <- rnorm(100,100)
    #data overall
    y <- c(y1,y2)
    hist(y) #clearly not normal
    hist(y1) #once we adjust though they look normal
    hist(y2)
    Clearly the overall data isn't normally distributed... but who cares. Once we look at each group individually they look normal so we're all right. This is why we only care if the residuals are normally distributed.

  2. #17
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    I don't know what the "&#37;" is in the Y-axis, but it looks like you plotted it wrong. You're supposed to plot the fitted values against the residuals.

    Edit: Looking at the graph again, I think I see what kind of graph it is. If it's what I think it is, then yes, you do have a problem.
    Last edited by Link; 08-16-2010 at 11:28 AM.

  3. #18
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    ok got it:
    Code: 
    http://i884.photobucket.com/albums/ac50/yoma819/fittedresiduals.jpg
    so we have assertained that i do infact need to transform my data.
    and advice on what kind of transformation?
    thanks again
    --yoma

  4. #19
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    Quote Originally Posted by Dason View Post
    The only reason we even care is because one of the assumptions we make when deriving the theory is that the errors are normally distributed. We don't care how the data itself is distributed because we say that once we adjust for our predictors the errors/residuals will be normally distributed.

    I guess one way to see why this is what we care about is consider we are comparing two groups.
    Code: 
    #data from first group
    y1 <- rnorm(100,5)
    #data from second group
    y2 <- rnorm(100,100)
    #data overall
    y <- c(y1,y2)
    hist(y) #clearly not normal
    hist(y1) #once we adjust though they look normal
    hist(y2)
    Clearly the overall data isn't normally distributed... but who cares. Once we look at each group individually they look normal so we're all right. This is why we only care if the residuals are normally distributed.
    ok i understand why i am looking at the residuals now , thanks for clearing that confusion up.
    i take it in your R code you are generating random data and putting it into y1?
    Code: 
    y1 <- rnorm(100,5)
    and then normally distributed data into y2

    Code: 
    y2 <- rnorm(100,100)
    but what does:
    Code: 
    y <- c(y1,y2)
    do?
    sorry just trying to understand your R code!
    cheers
    Yoma

  5. #20
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    does anyone know of any software that will transform data automatically (like quickfit does for distributions)
    i know quickfit is not 100% but it gives a great direction to go in and then further test.
    many thanks
    yoma

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