query: How to use eigenvectors to identify which variables are involved in collinearity?
positive_passages.docid: 22531
positive_passages.text: Testing for linear dependence among the columns of a matrix
negative_passages.docid: 106024
negative_passages.text: The eigenvalues and corresponding eigenvectors for the matrix
negative_passages.docid: 154667
negative_passages.text: Singular values of the data matrix and eigenvalues of the covariance matrix
negative_passages.docid: 45866
negative_passages.text: How to infer correlations from correlations
negative_passages.docid: 53993
negative_passages.text: Can Eigenvalues[] and Eigenvectors[] be assumed to return the same ordering?
negative_passages.docid: 133568
negative_passages.text: Is it possible to have a basis for a covariance matrix such that the greatest variance is greater than the variance of the first eigenvector?
negative_passages.docid: 102317
negative_passages.text: Is there a geometrical interpretation to the notion of eigenvector and eigenvalues?
negative_passages.docid: 3589
negative_passages.text: How to reverse PCA and reconstruct original variables from several principal components?
negative_passages.docid: 25535
negative_passages.text: Categorical Principal Component Analysis - using Count, Continuous, Ordinal variables together
negative_passages.docid: 157246
negative_passages.text: Adjoint matrix eigenvalues and eigenvectors
negative_passages.docid: 34539
negative_passages.text: How to intuitively understand eigenvalue and eigenvector?
negative_passages.docid: 70571
negative_passages.text: Obtaining covariance matrix from correlation matrix
negative_passages.docid: 112590
negative_passages.text: How to deal with multicollinearity when performing variable selection?
negative_passages.docid: 14475
negative_passages.text: When can we speak of collinearity
negative_passages.docid: 6293
negative_passages.text: Is there a way to use the covariance matrix to find coefficients for multiple regression?
negative_passages.docid: 77307
negative_passages.text: Why does inversion of a covariance matrix yield partial correlations between random variables?
negative_passages.docid: 27102
negative_passages.text: Combining two covariance matrices