When building regression models for forecasting, analysts often encounter the problem of multicollinearity or illconditioning in their data sets. In such cases, large variances and covariances can make subset selection and parameter estimation difficult to impossible.

Multicollinearity is a common problem when estimating linear or generalized linear models, including logistic regression and Cox regression. It occurs when there are high correlations among predictor variables, leading to unreliable and unstable estimates of regression coefficients.

High multicollinearity results from a linear relationship between your independent variables with a high degree of correlation but aren’t completely deterministic (in other words, they don’t have perfect correlation). It’s much more common than its perfect counterpart and can be equally problematic when it comes to estimating an econometric model.

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression.

WhitePaper Multicollinearity in Customer Satisfaction Research Jay L. Weiner, Ph. D. Senior Vice President, Director of Marketing Sciences Jane Tang Vice President, Marketing Sciences Editorial Board Leigh Admirand Julie Busch Tim Keiningham Design and Production Roland Clifford Barbara Day About Ipsos Loyalty Ipsos Loyalty is a global, specialized practice dedicated to helping companies.

In this chapter, we have discussed what is simple regression, what is multiple regression ,how to build simple linear regression ,multiple linear regression what are the most important metric that one should consider in output of a regression line, what is Multicollinearity how to detect, how to eliminate Multicollinearity, what is R square what is adjusted R square, difference between R.

There are several remedial measure to deal with the problem of multicollinearity such Prinicipal Component Regression, Ridge Regression, Stepwise Regression etc. However, in the present case, I’ll go for the exclusion of the variables for which the VIF values are above 10 and as well as the concerned variable logically seems to be redundant.