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Sagot :
Multicollinearity can be defined as when two independent variables are correlated, multicollinearity occurs. Reason: Only the interactions between the independent variables are considered multicollinear.
Multicollinearity is the prevalence of excessive intercorrelations amongst or extra impartial variables in a more than one regression model. Multicollinearity: Multicollinearity exists whilst or extra of the explanatory variables are extraordinarily correlated.
This is a hassle as it could be tough to disentangle which ones first-rate explains any shared variance with the outcome. It additionally indicates that the 2 variables may also in reality constitute the identical underlying factor. Multicollinearity takes place whilst or extra impartial variables are extraordinarily correlated with each other in a regression model. This method that an impartial variable may be anticipated from any other impartial variable in a regression model.
To learn more about Multicollinearity, refer: brainly.com/question/15856348
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