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  • Multicollinearity refers to the condition when two or more independent variables in a multiple regression are highly correlated with each other
  • There are two types of multicollinearity:
    • Perfect multicollinearity
      • One independent variable has perfect correlation with other independent variables
      • In this case, coefficients of the model cannot be determined
    • Imperfect multicollinearity
      • One or more independent variable is correlated to some degree (not perfect) to other independent variables
      • In this case, coefficients can be determined but might result in errors during test of statistical significance (chances of Type II error) 
    • Effects of Multicollinearity
      • There is greater probability that we will incorrectly conclude that a variable is not statistically significant (A type II error)

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