How do I Detect and Handle Multicollinearity When Building Models?

[#1]

High correlation in predictor variables can harm your linear regression model. This problem is called collinearity or multi-collinearity.  The first video is an overview of collinearity, how to understand it, how to detect it, how it is related to the VIF (Variance Inflation Factor) shown in JMP reports and how to interpret VIF.  The second video shows how to use Principal Components Analysis to build models that accurately account for collinearity.  The attached JMP Journal gives you the data used in the videos, so you can try the techniques.

• JMP Documentation on correlations and multivariate techniques
• JMP Documentation on PCA
• Four half-day public course from JMP Education Analyzing and Modeling Multidimensional Data. 
• Mastering JMP video and materials on Building Predictive Models for Correlated and High Dimensional Data

[hogi]

What determines the scale (range) on the X axis in a Leverage Plot?

[Di_Michelson]

@hogi, the scale of the axes on a leverage plot is set to the data scale after the plot is created. First, find the residuals for both models, then add the mean, then scale the axes to the data. For details, see Predictum's excellent video stored at Leverage Plots 2.mp4 on Vimeo. You might also be interested in the first video of the two-part series at Leverage Plots 1 on Vimeo. 

[hogi]

thanks a lot - very illustrative