Explore: Multicollinearity

statistics, multicollinearity or collinearity is a situation where the predictors in a regression model are linearly dependent. Perfect multicollinearity refers

reason for the use of sensitivity analysis is to detect multicollinearity. Multicollinearity is the phenomenon where the correlation between two explanatory

vector-of-ones variable were also present, this would result in perfect multicollinearity, so that the matrix inversion in the estimation algorithm would be

used to calculate it. This is the problem of multicollinearity in moderated regression. Multicollinearity tends to cause coefficients to be estimated with

regression is a general way of handling this issue. Lack of perfect multicollinearity in the predictors. For standard least squares estimation methods,

Analysis of covariance (ANCOVA) is a general linear model that blends ANOVA and regression. ANCOVA evaluates whether the means of a dependent variable

dependent. Short of perfect multicollinearity, parameter estimates may still be consistent; however, as multicollinearity rises the standard error around

ill-posed problems. It is particularly useful to mitigate the problem of multicollinearity in linear regression, which commonly occurs in models with large numbers

In practice, we rarely face perfect multicollinearity in a data set. More commonly, the issue of multicollinearity arises when there is a "strong linear

predictors has more variables than observations, and when there is multicollinearity among X values. By contrast, standard regression will fail in these