In this paper we intend to improve the explanatory power of regressions when the deletion method is used for the remedy of Multicolinearity. If one deletes the variable (s) that is (are) responsible for Multicolinearity, he loses some information that is not common between the deleted variable (s) and the other remaining variables in the regression. To improve this method, we run the deleted va...

The present article discusses the role of categorical variable in the problem of multicollinearity in linear regression model. It exposes the diagnostic tool condition number to linear regression models with categorical explanatory variables and analyzes how the dummy variables and choice of reference category can affect the degree of multicollinearity. Such an effect is analyzed analytically a...

Multicollinearity in logistic regression affects the variance of the maximum likelihood estimator negatively. In this study, Liu-type estimators are used to reduce the variance and overcome the multicollinearity by applying some existing ridge regression estimators to the case of logistic regression model. A Monte Carlo simulation is given to evaluate the performances of these estimators when t...

Due to its extensive applicability and computational ease, moderated multiple regression (MMR) has been widely employed to analyze interaction effects between 2 continuous predictor variables. Accordingly, considerable attention has been drawn toward the supposed multicollinearity problem between predictor variables and their cross-product term. This article attempts to clarify the misconceptio...

in this paper we intend to improve the explanatory power of regressions when the deletion method is used for the remedy of multicolinearity. if one deletes the variable (s) that is (are) responsible for multicolinearity, he loses some information that is not common between the deleted variable (s) and the other remaining variables in the regression. to improve this method, we run the deleted va...

2. Various Methods of Estimation under Severe Multicollinearity Conditions: In what follows, we give a brief account of some important methods of estimation under severe multicollinearity conditions: (i). The Restricted Least Squares (RLS) Estimator of β : If we can put some restriction on the linear combination of regression coefficients such that , R r β = then the RLS estimator of β denoted ...

The natural complexity of ecological communities regularly lures ecologists to collect elaborate data sets in which confounding factors are often present. Although multiple regression is commonly used in such cases to test the individual effects of many explanatory variables on a continuous response, the inherent collinearity (multicollinearity) of confounded explanatory variables encumbers ana...

Multicollinearity is typically thought of as a problem of large standard errors resulting from the near linear dependence of independent variables. One solution is to have more informative data, possibly in the form of a larger sample. In this paper I argue that this understanding of multicollinearity is only partially correct. The near collinearity of independent variables results in regressio...