Question

The problem of perfect multicolinearity can be alleviated by adding more observations to the regression.

Answer 1

Multicollinearity generally occurs when there are high correlations between two or more predictor variables. ... If the correlation coefficient, r, is exactly +1 or -1, this is called perfect multicollinearity. If r is close to or exactly -1 or +1, one of the variables should be removed from the model if at all possible.

The problem of perfect multicollinearity can't be solved by adding more observation.

We can deal multicollinearity with :

1. Remove some of the highly correlated independent variables.
2. Linearly combine the independent variables, such as adding them together.
3. Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.

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