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Thread: Logistic or OLS Regression?

  1. #1
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    Logistic or OLS Regression?

    I am new to statistics and learning a bit about regression and logistic "regression."

    The dependent variable for my data set is whether a sports team wins, ties or loses (thus, three outcomes). There are multiple observations per team (1 per match). My first thought was multinomial logistic regression, but I've been having some problems with this in R. However, I am under the impression that since these are categorical results (ie 1, 2, or 3; loss, tie or win) that I cannot use ordinary regression.

    What are the statistical disadvantages in using ordinary regression instead of logistic regression in this case? Is it absolutely incorrect to use ordinary regression for this type of data set?

    Thanks in advance for your help!


  2. #2
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    hlsmith's Avatar
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    Re: Logistic or OLS Regression?

    ordinary regression = linear?

    Big picture:
    What would be the average of loss, tie, win? How would you interpret that? Think about least squares and plotting the observed and expected value (loss, tie, win). How would you do that? Would it be better to interpret the probability of groups?

    Most likely you are looking at multinomial.
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  3. #3
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    rogojel's Avatar
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    Re: Logistic or OLS Regression?

    I would say that it is not right to use OLS in this case. The numbers 1, 2, 3 are a little more then names ( though ordered), You could have a quite a different result if you coded the three outcomes 1, 1.5 and 1000 rather then 1 2 3 and there is no reason to prefer one over the other. So, Ordinal logistic regression seems to be the best way to analyze this type of data.


  4. #4
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    Re: Logistic or OLS Regression?

    If your dependent variable only has 3 levels you would probably want to use either ordered logistic regression if it was ordinal data or multinomial logistic regression if it is nominal.
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