DistributionMarket Strategy X3 Measurement of Variables
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Analyzing correlation matrix among independent variables. If there is high autocorrelation usually above 0.90 among independent variables, this
indicates multicollinearity appears. Whilst the relatively fair correlation among independent variables does not also mean no multicollinearity. It can
be affected due to effect of combination of two or more independent variables. Multicollinearity also can be drawn from 1 tolerance value and the opposite
2 variance inflation factor VIF. Both measurement can predict which independent variable explained by another variables. In modest
interpretation, each independent variable bound to dependent one and is regressed towards other independent variables. In addition, tolerance measures
variability of chosen independent variables which is not explained by other independent variables. Therefore, a small tolerance score equals to high VIF
score because VIF = 1Tolerance. A commonly used cut-off score to indicate multicollinearity is Tolerance score
≤ 0.10 or simply equals to VIF score ≥ 10. Every researcher should determine collinearity level which can be tolerated.
In addition, a regression model can be said free from multicollinearity if correlation coefficient among independent variables should be lower than 0.5. if the
correlation so strong, multicollinearity exists. Furthermore if it occurs, Santoso 2010: 207 suggests:
Dropping out one of variables, for instance independent variable A and B is strongly correlated each other, so the researcher may determine if variable A
or B to be dropped from regression model. Using advanced method, such as Bayesian regression or Ridge regression.
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