BACHELOR OF BUSINESS ADMINISTRATION

Detailed explanation-1: -Multicollinearity is the occurrence of high intercorrelations among two or more independent variables in a multiple regression model.

Detailed explanation-2: -Multicollinearity refers to a situation in which more than two explanatory variables in a multiple regression model are highly linearly related. There is perfect multicollinearity if, for example as in the equation above, the correlation between two independent variables equals 1 or −1.

Detailed explanation-3: -Multicollinearity occurs when independent variables in a regression model are correlated. This correlation is a problem because independent variables should be independent.

Detailed explanation-4: -Multicollinearity exists when two or more of the predictors in a regression model are moderately or highly correlated with one another. Unfortunately, when it exists, it can wreak havoc on our analysis and thereby limit the research conclusions we can draw.

Detailed explanation-5: -Multicollinearity is a situation where two or more predictors are highly linearly related. In general, an absolute correlation coefficient of >0.7 among two or more predictors indicates the presence of multicollinearity.