Abstract
Multicollinearity is the effect of having too high correlation between variables. At its extreme, there may be perfect correlation which results in a singular (determinant equal to zero) correlation matrix. However, even when we do not reach this extreme, multicollinearity causes problems: because of the redundancy in the variables, we may have an ill-conditioned correlation matrix which is very prone to large variances which may be due to noise in the data set.
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© 2005 Springer-Verlag London Limited
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(2005). Multicollinearity and Partial Least Squares. In: Hebbian Learning and Negative Feedback Networks. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-118-0_13
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DOI: https://doi.org/10.1007/1-84628-118-0_13
Publisher Name: Springer, London
Print ISBN: 978-1-85233-883-1
Online ISBN: 978-1-84628-118-1
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