query: How to identify which variables are collinear in a singular regression matrix?
positive_passages.docid: 22531
positive_passages.text: Testing for linear dependence among the columns of a matrix
negative_passages.docid: 42427
negative_passages.text: Understanding linear algebra in Ordinary Least Squares derivation
negative_passages.docid: 124337
negative_passages.text: Degrees of freedom in regression
negative_passages.docid: 130286
negative_passages.text: Problem with Singular Value Decomposition
negative_passages.docid: 7193
negative_passages.text: Textbooks on linear regression with least squares
negative_passages.docid: 29006
negative_passages.text: Kernel ridge regression with matrix-vector data set $S := \{ X_i, y_i \}_{i=1}^{N}$?
negative_passages.docid: 59668
negative_passages.text: Regression coefficients that flip sign after including other predictors
negative_passages.docid: 28567
negative_passages.text: Hat matrix with simple linear regression
negative_passages.docid: 15444
negative_passages.text: Linear Regression with individual constraints in R
negative_passages.docid: 37653
negative_passages.text: Singular covariance matrix in Mahalanobis distance in Matlab
negative_passages.docid: 75298
negative_passages.text: Standardization of variables and collinearity
negative_passages.docid: 98433
negative_passages.text: Closed form solution for slope coefficients in bivariate regression
negative_passages.docid: 87448
negative_passages.text: Why is a sample covariance matrix singular when sample size is less than number of variables?
negative_passages.docid: 118276
negative_passages.text: Multivariate multiple regression validation
negative_passages.docid: 92434
negative_passages.text: Hat matrix and leverages in classical multiple regression
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: 89729
negative_passages.text: If $X$ is a singular matrix, then is $X'X$ singular?