collinearity

Examples of collinearity in the following topics:

• Including and assessing many variables in a model

  • We say the two predictor variables are collinear (pronounced as co-linear) when they are correlated, and this collinearity complicates model estimation.
  • While it is impossible to prevent collinearity from arising in observational data, experiments are usually designed to prevent predictors from being collinear.

• Some Pitfalls: Estimability, Multicollinearity, and Extrapolation

  • As with collinearity, this is less important if one is only interested in a predictive model - but even when researchers say they are only interested in prediction, we find they are usually just as interested in the relative importance of the different explanatory variables.

• Multiple and logistic regression solutions

  • (b) When coefficient estimates are sensitive to which variables are included in the model, this typically indicates that some variables are collinear.

• Coefficient of Determination

  • there is collinearity present in the data on the explanatory variables; or

• Logistic regression

  • (b) When coefficient estimates are sensitive to which variables are included in the model, this typically indicates that some variables are collinear.

• Two model selection strategies

  • It is common for p-values of one variable to change, due to collinearity, after eliminating a different variable.