On Multico Uinearity
|•||Consequences of Multico Uinearity|
|•||Multico Uinearity Misconceived|
Multico Uinearity is a big word to describe a simple concept; it means that independent variables are correlated (e.g., rX1X2<> 0). This chapter elaborates on chapter 11, which introduced multico Uinearity in a multiple correlation/regression context. It also extends chapters 12 and 13, which described statistical inference. It begins with a brief summary of consequences of multico Uinearity. These consequences are then illustrated through a simulation. Next, a common multico Uinearity misconception is identified. Finally, suggestions for addressing multico Uinearity are offered.
Chapter 11 explained that the multiple coefficient of determination (P2YX1X2 is not equal to the sum of the simple coefficients of determination (ρ2YX1 + ρ2YX2)b wnen there is multicollinearity