# 12.4 - Detecting Multicollinearity Using Variance Inflation Factors

Okay, now that we know the effects that multicollinearity can have on our regression analyses and subsequent conclusions, how do we tell when it exists? That is, how can we tell if multicollinearity is present in our data?

Some of the common methods used for detecting multicollinearity include:

- The analysis exhibits the signs of multicollinearity — such as, estimates of the coefficients vary from model to model.
- The
*t*-tests for each of the individual slopes are non-significant (*P*> 0.05), but the overall*F*-test for testing all of the slopes are simultaneously 0 is significant (*P*< 0.05). - The correlations among pairs of predictor variables are large.

Looking at correlations only among *pairs* of predictors, however, is limiting. It is possible that the pairwise correlations are small, and yet a linear dependence exists among three or even more variables, for example, if *X _{3}* = 2

*X*

_{1}+ 5

*X*

_{2}+

*error*, say. That's why many regression analysts often rely on what are called

**variance inflation factors**(

*VIF*) to help detect multicollinearity.

### What is a Variation Inflation Factor?

As the name suggests, a variance inflation factor (*VIF*) quantifies how much the variance is inflated. But what variance? Recall that we learned previously that the standard errors — and hence the variances — of the estimated coefficients are inflated when multicollinearity exists. So, the variance inflation factor for the estimated coefficient *b _{k}* —denoted

*VIF*

_{k}_{ }—is just the factor by which the variance is inflated.

Let's be a little more concrete. For the model in which *x _{k}* is the only predictor:

\[y_i=\beta_0+\beta_kx_{ik}+\epsilon_i\]

it can be shown that the variance of the estimated coefficient *b _{k}* is:

\[Var(b_k)_{min}=\frac{\sigma^2}{\sum_{i=1}^{n}(x_{ik}-\bar{x}_k)^2}\]

Note that we add the subscript "min" in order to denote that it is the smallest the variance can be. Don't worry about how this variance is derived — we just need to keep track of this baseline variance, so we can see how much the variance of *b _{k}* is inflated when we add correlated predictors to our regression model.

Let's consider such a model with correlated predictors:

\[y_i=\beta_0+\beta_1x_{i1}+ \cdots + \beta_kx_{ik}+\cdots +\beta_{p-1}x_{i, p-1} +\epsilon_i\]

Now, again, if some of the predictors are correlated with the predictor *x _{k}*, then the variance of

*b*is inflated. It can be shown that the variance of

_{k}*b*is:

_{k}\[Var(b_k)=\frac{\sigma^2}{\sum_{i=1}^{n}(x_{ik}-\bar{x}_k)^2}\times \frac{1}{1-R_{k}^{2}}\]

where \(R_{k}^{2}\) is the *R*^{2}-value obtained by regressing the *k ^{th}* predictor on the remaining predictors. Of course, the greater the linear dependence among the predictor

*x*and the other predictors, the larger the \(R_{k}^{2}\) value. And, as the above formula suggests, the larger the \(R_{k}^{2}\) value, the larger the variance of

_{k}*b*.

_{k}How much larger? To answer this question, all we need to do is take the ratio of the two variances. Doing so, we obtain:

\[\frac{Var(b_k)}{Var(b_k)_{min}}=\frac{\left( \frac{\sigma^2}{\sum(x_{ik}-\bar{x}_k)^2}\times \frac{1}{1-R_{k}^{2}} \right)}{\left( \frac{\sigma^2}{\sum(x_{ik}-\bar{x}_k)^2} \right)}=\frac{1}{1-R_{k}^{2}}\]

The above quantity is what is deemed the variance inflation factor for the *k ^{th}* predictor. That is:

\[VIF_k=\frac{1}{1-R_{k}^{2}}\]

where \(R_{k}^{2}\) is the *R*^{2}-value obtained by regressing the *k ^{th}* predictor on the remaining predictors. Note that a variance inflation factor exists for

*each of the k predictors*in a multiple regression model.

How do we interpret the variance inflation factors for a regression model? Again, it is a measure of how much the variance of the estimated regression coefficient *b _{k}* is "inflated" by the existence of correlation among the predictor variables in the model. A VIF of 1 means that there is no correlation among the

*k*predictor and the remaining predictor variables, and hence the variance of

^{th}*b*is not inflated at all. The general rule of thumb is that VIFs exceeding 4 warrant further investigation, while VIFs exceeding 10 are signs of serious multicollinearity requiring correction.

_{k}### An Example

Let's return to the blood pressure data (bloodpress.txt) in which researchers observed the following data on 20 individuals with high blood pressure:

- blood pressure (
*y*=*BP*, in mm Hg) - age (
*x*_{1}=*Age*, in years) - weight (
*x*_{2}=*Weight*, in kg) - body surface area (
*x*_{3}=*BSA*, in sq m) - duration of hypertension (
*x*_{4}=*Dur*, in years) - basal pulse (
*x*_{5}=*Pulse*, in beats per minute) - stress index (
*x*_{6}=*Stress*)

As you may recall, the matrix plot of *BP*, *Age*, *Weight*, and *BSA*:

the matrix plot of *BP*, *Dur*, *Pulse*, and *Stress*:

and the correlation matrix:

suggest that some of the predictors are at least moderately marginally correlated. For example, body surface area (BSA) and weight are strongly correlated (*r* = 0.875), and weight and pulse are fairly strongly correlated (*r* = 0.659). On the other hand, none of the pairwise correlations among age, weight, duration and stress are particularly strong (*r* < 0.40 in each case).

Regressing *y* = BP on all six of the predictors, we obtain:

[Minitab v17 reports the variance inflation factors by default; for v16 you have to select this under Options.] As you can see, three of the variance inflation factors —8.42, 5.33, and 4.41 —are fairly large. The VIF for the predictor *Weight*, for example, tells us that the variance of the estimated coefficient of *Weight* is inflated by a factor of 8.42 because *Weight* is highly correlated with at least one of the other predictors in the model.

For the sake of understanding, let's verify the calculation of the VIF for the predictor *Weight*. Regressing the predictor *x*_{2} = *Weight* on the remaining five predictors:

Minitab reports that \(R_{Weight}^{2}\) is 88.1% or, in decimal form, 0.881. Therefore, the variance inflation factor for the estimated coefficient *Weight* is by definition:

\[VIF_{Weight}=\frac{Var(b_{Weight})}{Var(b_{Weight})_{min}}=\frac{1}{1-R_{Weight}^{2}}=\frac{1}{1-0.881}=8.4\]

Again, this variance inflation factor tells us that the variance of the weight coefficient is inflated by a factor of 8.4 because *Weight* is highly correlated with at least one of the other predictors in the model.

So, what to do? One solution to dealing with multicollinearity is to remove some of the violating predictors from the model. If we review the pairwise correlations again:

we see that the predictors *Weight* and *BSA* are highly correlated (*r* = 0.875). We can choose to remove either predictor from the model. The decision of which one to remove is often a scientific or practical one. For example, if the researchers here are interested in using their final model to predict the blood pressure of future individuals, their choice should be clear. Which of the two measurements — body surface area or weight — do you think would be easier to obtain?! If indeed weight is an easier measurement to obtain than body surface area, then the researchers would be well-advised to remove *BSA* from the model and leave *Weight* in the model.

Reviewing again the above pairwise correlations, we see that the predictor *Pulse* also appears to exhibit fairly strong marginal correlations with several of the predictors, including *Age* (*r* = 0.619), *Weight* (*r* = 0.659) and *Stress* (*r* = 0.506). Therefore, the researchers could also consider removing the predictor *Pulse* from the model.

Let's see how the researchers would do. Regressing the response *y* = *BP* on the four remaining predictors* age*, *weight*, *duration* and *stress*, we obtain:

Aha — the remaining variance inflation factors are quite satisfactory! That is, it appears as if hardly any variance inflation remains. Incidentally, in terms of the adjusted *R*^{2}-value, we did not seem to lose much by dropping the two predictors *BSA* and *Pulse* from our model. The adjusted *R*^{2}-value decreased to only 98.97% from the original adjusted *R*^{2}-value of 99.44%.

## PRACTICE PROBLEMS: Variance inflation factors
We’ll use the cement.txt data set to explore variance inflation factors. The response 1. Use the Stat >> Basic Statistics >> Correlation ... command in Minitab to get an idea of the extent to which the predictor variables are (pairwise) correlated. Also, use the Graph >> Matrix Plot ... command in Minitab to get a visual portrayal of the (pairwise) relationships among the response and predictor variables. 2. Regress the fourth predictor, x_{1}, x_{2}, and x_{3}. That is, fit the linear regression model treating x as the response and _{4}x_{1}, x_{2}, and x_{3} as the predictors. What is the R^{2}_{4} value? (Note that Minitab rounds the R^{2} value it reports to three decimal places. For the purposes of the next question, you’ll want a more accurate R^{2} value. Calculate the R^{2} value SSR using its definition, \(\frac{SSR}{SSTO}\). Use your calculated value, carried out to 5 decimal places, in answering the next question.)3. Using your calculated 4. Minitab will actually calculate the variance inflation factors for you. Fit the multiple linear regression model with VIF_{4} that you calculated consistent with what Minitab reports?5. Note that all of the t-tests for testing the individual H_{0} : β = 0 and the result of the overall F-test for testing H_{i}_{0} : β_{1} = β_{2} = β_{3} = β_{4} = 0? Why does this happen?6. We learned that one way of reducing data-based multicollinearity is to remove some of the violating predictors from the model. Fit the linear regression model with |