The first time I read John Cook’s advice “Don’t invert that matrix,” I wasn’t sure how to follow it. I was familiar with manipulating matrices analytically (with pencil and paper) for statistical derivations, but not with implementation details in software. For reference, here are some simple examples in MATLAB and R, showing what to avoid and what to do instead.

[*Edit: R code examples and results have been revised based on Nicholas Nagle’s comment below and advice from Ryan Tibshirani.*]

If possible, John says, you should just ask your scientific computing software to directly solve the linear system . This is often faster and more numerically accurate than computing the matrix inverse of A and then computing .

We’ll chug through a computation example below, to illustrate the difference between these two methods. But first, let’s start with some context: a common statistical situation where you may **think** you need matrix inversion, even though you really don’t.

[*One more edit: I’ve been guilty of inverting matrices directly, and it’s never caused a problem in my one-off data analyses. As Ben Klemens comments below, this may be overkill for most statisticians. But if you’re writing a package, which many people will use on datasets of varying sizes and structures, it may well be worth the extra effort to use solve or QR instead of inverting a matrix if you can help it.*]