If there is no linear relationship between the regressors, they are said to be orthogonal. Multicollinearity is a case of multiple regression in which the predictor variables are themselves highly correlated. If the goal is to understand how the various X variables impact Y, then multicollinearity is a big problem. Multicollinearity is a matter of degree, not a matter of presence or absence. In...

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater at...

The application of the lasso is espoused in high-dimensional settings where only a small number of the regression coefficients are believed to be nonzero (i.e., the solution is sparse). Moreover, statistical properties of high-dimensional lasso estimators are often proved under the assumption that the correlation between the predictors is bounded. In this vein, coordinatewise methods, the most ...

Multicollinearity can be briefly described as the phenomenon in which two or more identified predictor variables in a multiple regression model are highly correlated. The presence of this phenomenon can have a negative impact on the analysis as a whole and can severely limit the conclusions of the research study. This paper reviews and provides examples of the different ways in which multicolli...

The objective of this work was evaluate multicollinearity effect and discard the variables which are based on multicollinearity reduction in diversity analysis of common bean genotypes, related to seeds physiological quality, in different salinity levels in germination substrate. The common bean seed germination test for six cultivars and seven landrace genotypes was performed in paper rolls (g...

Multicollinearity is a topic that is often discussed accordingly with linear regression analysis. One of the assumptions for a multiple linear regression model is that the predictor variables, which make up the X matrix, are assumed to be uncorrelated. This assumption ensures the errors from the ordinary least squares estimators will be uncorrelated. However, true independence amongst the predi...

Although fuzzy regression is widely employed to solve many problems in practice, what seems to be lacking is the problem of multicollinearity. In this paper, the fuzzy centers principal component analysis is proposed to first derive the fuzzy principal component scores. Then the fuzzy principal component regression (FPCR) is formed to overcome the problem of multicollinearity in the fuzzy regre...

In this section, we address the problem of multicollinearity in multiple regression analysisthat appeared in the article titled, " Correlation between frailty and cognitive function in non-demented community dwelling older Koreans, " published in November 2014 by Kim et al. This is one of the most frequent comments made about articles usingmultiple regression analysis. Multicollinearity indicat...