In the book I use the car package to get VIF and other multicollinearity diagnostics. I’ve occasionally found this breaks down (usually through mixing different versions of R on different machines at work home or on the move). I recently saw the mctest package and thought it would be useful to use that as a […]
Here's my (slightly delayed) August reading list:Ahelegbey, A. F., 2015. The econometrics of networks: A review. Working Paper 2015/13, Department of Economics, University of Venice.Clemens, M. A., 2015. The meaning of failed replications: A revi...
I just love Dick Startz's "byline" on the EViews 9 Beta Forum: "Non-normality and collinearity are NOT problems!"Why do I like it so much? Regarding "normality", see here, and here. As for "collinearity": see here, here, here, and &...
This post is essentially an extension of another recent post on this blog. I'll assume that you've read that post, where I discussed the problem of solving linear equations of the form Ax = y, when the matrix A is singular.Let's look at how this proble...
You can only invert a matrix if that matrix is non-singular. Right? Actually, that's wrong.You see, there are various sorts of inverse matrices, and most of them apply to the situation where the original matrix is singular. Before elaborating on t...
Any model in statistics or machine learning aims to capture the underlying trend or systematic component in a data set. That underlying trend cannot be precisely captured because of the random variation in the data around that trend. A model must have enough complexity to capture that trend, but not too much complexity to capture […]
I just posted brief multicollinearity tutorial on my other blog (loosely based on the material from the Serious Stats book). You can read it here.Filed under: serious stats, stats advice Tagged: correlation and covariance, general linear model, messy d...
I just posted brief multicollinearity tutorial on my other blog (loosely based on the material from the Serious Stats book). You can read it here.Filed under: serious stats, stats advice Tagged: correlation and covariance, general linear model, messy d...
Multicollinearity said in "plain English" is redundancy. Unfortunately, it isn't quite that simple, but it's a good place to start. Put simply, multicollinearity is when two or more predictors in a regression are highly related to one another, such that they do not provide unique and/or independent information to the regression.A common example that is used to demonstrate this idea is having both height and weight as predictors in a…
