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**Statistical Thinking for Industrial Problem Solving**

In this video, we use the **Bodyfat** data to explore multicollinearity in JMP using all the potential predictors.

Recall that, in this scenario, we are interested in predicting **%Fat** as a function of several physical measurements.

We’ll start by exploring the data.

We’ll use the Multivariate platform from Analyze, Multivariate Methods to produce both a correlation matrix and a scatterplot matrix.

As expected, many of the bivariate correlations are extremely high.

Let’s see if multicollinearity is a problem when we fit a regression model.

We’ll use Fit Model to fit a model for **%Fat**, with all the potential predictors, **Age** through **Wrist**.

VIFs are available from the Parameter Estimates panel. To request VIFs, right-click on the panel and select **Columns** and then **VIF**.

Some of the VIFs are extremely high. Recall that we use a cutoff of 5 or 10 to indicate there is a problem with multicollinearity.

What happens if we remove **BMI**? As we discussed earlier, removing **BMI** might make sense, given that **BMI** is a function of both **Weight** and **Height**.

We’ll remove **BMI** using the Effect Summary table.

Let’s look at the VIFs again to see whether this has addressed the .

The VIF for **Weight** is still high.

Let’s remove **Weight** from the model.

Some of the VIFs are slightly over 10, but most of the VIFs are now much lower.

In this example, we decided to remove **BMI** and **Weight** from the model based on VIFs, and the issue with multicollinearity was, for the most part, resolved.