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    Please can someone explain to me what benefit multivariate analysis has when I am measuring 100 different variables? Thanks
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    (Original post by jsmith6131)
    Please can someone explain to me what benefit multivariate analysis has when I am measuring 100 different variables? Thanks
    It's a little hard to say very much without more detail about your specific problem, but there are some introductory slides that I found here which have a pretty good introduction to the whys and hows of multivariate analysis.

    Tell us more about what you're trying to do, and I'll attempt to say more, if you like.
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    (Original post by Gregorius)
    It's a little hard to say very much without more detail about your specific problem, but there are some introductory slides that I found here which have a pretty good introduction to the whys and hows of multivariate analysis.

    Tell us more about what you're trying to do, and I'll attempt to say more, if you like.
    Ok so it relates to my previous question that you kindly helped me with
    (http://www.thestudentroom.co.uk/show...121&highlight=)

    You have two populations X and Y from which you have drawn random samples A and B respectively. A set of n parameters are measured in both A and B; some sort of intervention is applied to both A and B and the n parameters are measured again in A and B. This results in measurements x_{1,1}^A, x_{2,1}^A, \cdots, x_{n,1}^A and x_{1,2}^A, x_{2,2}^A, \cdots, x_{n,2}^A made in A before and after the intervention and x_{1,1}^B, x_{2,1}^B, \cdots, x_{n,1}^B and x_{1,2}^B, x_{2,2}^B, \cdots, x_{n,2}^B made in B before and after the intervention. The effect of the intervention is measured in A and B separately by calculating p-values for the differences x_{i,2}^A - x_{i, 1}^A and x_{i,2}^B - x_{i, 1}^B. This gives you p-values  p_{i}^A and  p_{i}^B. You now wish to find which of the parameters x_{1}, x_{2}, \cdots, x_{n} is "most significantly" changed in A whilst being "least significantly" changed in B and vice-versa.
    I was previously trying to find the best single parameter to differentiate the two populations. I want to see now if there is a GROUP of the n parameters that can distinguish the two groups and I think multivariate analysis might be able to do this but I wanted to check what the benefit of multivariate analysis is before I make this assumption
    thanks
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    (Original post by jsmith6131)
    I was previously trying to find the best single parameter to differentiate the two populations. I want to see now if there is a GROUP of the n parameters that can distinguish the two groups and I think multivariate analysis might be able to do this but I wanted to check what the benefit of multivariate analysis is before I make this assumption
    thanks
    I don't immediately see how this would work, given the framework that we've explored so far. When you do multivariate analysis you're looking at multiple correlated outcomes and seeing how they co-vary with changing explanatory variables. Unless there's more structure to the problem, there doesn't seem to be enough to get this sort of thing going.
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    (Original post by Gregorius)
    I don't immediately see how this would work, given the framework that we've explored so far. When you do multivariate analysis you're looking at multiple correlated outcomes and seeing how they co-vary with changing explanatory variables. Unless there's more structure to the problem, there doesn't seem to be enough to get this sort of thing going.
    Well I performed a univariate analysis (Mann Whitney U) on the two groups and found 50 of the extracted featyres features were significantly different between them. Could I not do multivariate analysis to reduce this number down to 5 or 10?
    Or is this an example of princple components analysis?
 
 
 
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