Classical regression analysis uses partial coefficients to measure the influences of some variables (regressors) on another variable (regressand). However, a descriptive point of view shows that these coefficients are very bad measures of influence. Their interpretation as an average change of the regressand is only valid if the regressors are weakly correlated, and they are useless when the de...

This paper demonstrates that measurement error can conspire with multicollinearity among explanatory variables to mislead an investigator. A causal variable measured with error may be overlooked and its significance transferred to a surrogate. The latter’s significance can then be entirely spurious, in that controlling it will not predictably change the response variable. In epidemiological res...

The logistic regression model is used to predict a binary response variable in terms of a set of explicative ones. The estimation of the model parameters is not too accurate and their interpretation in terms of odds ratios may be erroneous, when there is multicollinearity (high dependence) among the predictors. Other important problem is the great number of explicative variables usually needed ...

When data from different networks are merged for mapping purpose, biases may appear and lead to unrealistic results. In this paper we define a geostatistical model that generalizes the universal kriging model such that it can handle heterogeneous data. Multiple bias sources can be treated simultaneously through the notion of bias factors. The associated best linear unbiased estimation and predi...

the latest known source of multicollinearity, a nonorthogonality of two or more explanatory variables in multiple regression models, is high leverage points. Interpreting a fitted regression model may become impossible by the influential impacts of multicollinearity. In this paper, we attempt to investigate the impact of different sample sizes as one of the main causing factors of high leverage...

Multiple regression is a widely used technique to study complex interrelationships among people, information, and technology. In the face of multicollinearity, researchers encounter challenges when interpreting multiple linear regression results. Although standardized function and structure coefficients provide insight into the latent variable ( ) produced, they fall short when researchers want...

Assessing the harmful effects of multicollinearity in a regression model with multiple predictors has always been one of the great problems in applied econometrics. As correlations amongst predictors are almost always present to some extent (especially in time-series data generated by natural experiments), the question is at what point does inter-correlation become harmful. Despite receiving qu...

In presence of multicollinearity principal component regression (PCR) is sometimes suggested for the estimation of the regression coefficients of a multiple regression model. Due to ambiguities in the interpretation involved by the orthogonal transformation of the set of explanatory variables the method could not yet gain wide acceptance. Factor analysis regression (FAR) provides a model-based ...