Collinearity

Collinearity
A set of variables is said to exhibit collinearity if some variables are approximate, or worse, exact linear combinations of some other variables. Therefore, collinearity is a telltale of linear redundency in the data.

Multicollinearity and Matrix Ill-Conditioning
This is a common problem in many correlation analyses. Imagine that you have two predictors (X variables) of a person's height: (1) weight in pounds and (2) weight in ounces.

Missing values (blanks, spaces, nulls)
Outlier values
Collinearity assessment (related to correlations between predictor variables)
Frequencies of multiple codes in a given variable; ...

As with other types of regression, there is no need for the independent variables to be statistically independent from each other (unlike, for example, in a Naive Bayes classifier); however, collinearity is assumed to be relatively low, ...

See also: Regression, Distribution, Residual, Confidence interval, Variance