Various formal and informal diagnostics may help detect multicollinearity. There are also some methods designed to deal with multicollinearity. In cases of severe multicollinearity, it may not be possible to calculate some of the diagnostic measures of influence or leverage , or even to perform the fit itself. In such cases, the data are said to be ill-conditioned . Values may not be identically distributed because of the presence of outliers .
Outliers are anomalous values in the data.
Outliers may have a strong influence over the fitted coefficients, giving a poor fit to the bulk of the data observations. Outliers tend to increase the estimate of residual variance, lowering the chance of rejecting the null hypothesis . They may be due to recording errors, which may be correctable, or they may be due to the Y values not all being sampled from the same population. Apparent outliers may also be due to the Y values being from the same, but nonnormal , population. Outliers may show up clearly in a scatterplot of Y and one of the X variables, as points that do not lie near the general trend of the data.