# Model based on many variables

Let's say I have a matrix of values for many different variables Y1..Y1000 at X=1,2,3..,10. Some of these variables are directly correlated with X, some follow different shapes (e.g. a normal distribution) and some are just random. I want to build a model to predict X based on given values of Y1..Y1000.

What would be the correct approach for this? I assume a simple linear regression would not be feasible because of the number of variables and the fact that not all variables are linearly dependent on X.

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 I'm having trouble following this. First, do you have 100 or 1000 variables labeled "Y"? Are you using "Y" for the predictor variables & "X" for the response variable? (That's OK, but it's the opposite of how it's usually done.) Is the main issue here that you have a multivariate situation (ie, multiple response variables)? Is this supposed to be a $p>>n$ problem? Re different shapes, note that the distribution of predictors is irrelevant, but if the issue is w/ response variables, can you say more about what the distributions are? (Eg, I think a random variable can be normally distributed.) – gung Jul 19 '12 at 19:38 I'm sorry that I wasn't clear enough, my stats vocabulary is a bit rusty... The 100 was a typo, that should have been 1000 in both cases. So just to reiterate, I have a set of data that tells me how Y1..Y1000 behave for different known values of X and I want to build a model "X ~ Y1, Y2, Y3, ...", to predict X based on values of Y. So I think X would be the dependent/response variable and Y the independent/predictor variable. Sorry if I mislabeled them. – user12622 Jul 19 '12 at 19:56 So in general I think the problem is that I only have around 10 data points per variable but a lot of variables. – user12622 Jul 19 '12 at 19:59