In multiple regression analysis, a situation in which at least some independent variables in a set are highly correlated with each other.
[multi- + L. col-lineo, to line up together]
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The correlation matrix was used in this analysis to help illuminate where potential multicolinearity between variables may be present.
In addition, predictor variables were evaluated for multicolinearity, using pairwise scatterplots, variance inflation factors, and condition indexes.
From this table, we can observe low correlations among the independent variables, indicating that multicolinearity is not a problem.
This test has been done to review the Multicolinearity between the independent variables with the following statistical hypothesis:
The book addresses these questions by exploring the descriptive statics, the correlations analysis, the multicolinearity analysis and the regression analysis.
Finally, analysis for multicolinearity suggests that this is not likely to be an undue influence on the results.
80, the usual threshold for multicolinearity problems.
The IBM variables are independent from one another, thus limiting concerns of multicolinearity.
Thus, the three HBM variables are independent from one another, limiting concerns of multicolinearity (Table 2).
Preliminary analysis ensured that there were no violations of sample size, multicolinearity and outliers.
A correlation test was carried out between the independent variables to remove multicolinearity problems.