BACHELOR OF BUSINESS ADMINISTRATION

Detailed explanation-1: -Multicollinearity occurs when independent variables in a regression model are correlated. This correlation is a problem because independent variables should be independent. If the degree of correlation between variables is high enough, it can cause problems when you fit the model and interpret the results.

Detailed explanation-2: -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.

Detailed explanation-3: -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-4: -Correlated independent variables make inferences about individual regression coefficients difficult. What level of correlation between two independent variables in a regression model generally will cause multicollinearity problems. ✅ A correlation coefficient less than-0.7.