It is not totally clear what you are trying to do. Depending on the situation there are several different strategies that you could try.
If you have some kind of cost associated with the vector values, you can use it to sort the…"
sometimes it is not possible to avoid the multicollinearity. Especially if the correleated variables are categorical. One thing that you could try is to transform the correltated variables using for example PCA to form new variables that are not…"
I would say that and R square of 50 would indicate that the covariates are related to the dependent variable, but this does not say that the model is accurate enoguh for you situation.
There is no general way of saying when the model…"
If most of your correlated variables are categorical like citizenship and nationality then the reason that the having both in the model gives better results is that the difference in the model is meanigful for the case. That citizenship and…"
Regression models can become ustable if the variables included have strong correlations.
If you wan to include all the variables but but want to avoid the problems that come from correlated variables you could use principal component analysis…"
"The r(i,j) is the rank( index in the ordered list of items) that you get when you order the distances from point i to other points.
In matlab you could put the distances from point i to the other points to a vector and then sort the vector base on…"
I have a Phd in Computer Science and my academic work has been mostly related to information visualization. Especially to nonlinear projections and how to estimate the quality of visualizations they produce.
Currently I'm working as an consultant dealing with all kinds of analytical (and some not so analytical :) problems our customers have.
Field of Expertise:
Business Analytics, Predictive Modeling, Data Mining, Vizualization, Other, SAS
Years of Experience in Analytical Role:
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