# How much Multicollinearity is too much?

**multicollinearity**is that you have

**too much**when the VIF is greater than 10 (this is probably because we have 10 fingers, so take such rules of thumb for what they're worth). The implication would be that you have

**too much collinearity**between two variables if r≥. 95.

In respect to this, what is the limit for VIF values?

Various recommendations for acceptable levels of **VIF** have been published in the literature. Perhaps most commonly, a **value** of 10 has bee recommended as the **maximum** level of **VIF** (e.g., Hair, Anderson, Tatham, & Black, 1995; Kennedy, 1992; Marquardt, 1970; Neter, Wasserman, & Kutner, 1989).

**High**degree: If the coefficient value lies between ± 0.50 and ± 1, then it is said to be a strong

**correlation**. Moderate degree: If the value lies between ± 0.30 and ± 0.49, then it is said to be a medium

**correlation**. Low degree: When the value lies below + . 29, then it is said to be a small

**correlation**.

Also to know is, what is considered high Multicollinearity?

**Multicollinearity** occurs when two or more predictors in the model are correlated and provide redundant information about the response. If VIF value exceeding 4.0, or by tol- erance less than 0.2 then there is a problem with **multicollinearity** (Hair et al., 2010).

**Multicollinearity** can also be detected with the help of tolerance and its reciprocal, called variance inflation factor (VIF). If the value of tolerance is less than 0.2 or 0.1 and, simultaneously, the value of VIF 10 and above, then the **multicollinearity** is problematic.