A short five minute video has been created explaining least squares with JMP. The author, Lee Creighton, uses a very simple example of fitting a line to data, and considers different measures of a “best” fit (not to ruin the punch line, but least squares has a global minimum). On the right hand side of the applet is an error bar (for the particular measure being considered) that changes as the line is changed for the set of data points.

Least squares considered with, well, squares!

What I like about the visualization is the that the squared errors are displayed as actual squares. It seems silly, but I had never thought of squared errors in a literal, geometric way. I always thought of them as scalars, and nothing more. But showing actual squares makes for a much better visual representation.

It’s likely we’ll see more video tutorials from Lee Creighton on vimeo, as he’s added a few new items already (although there’s been no mention of these at the JMP blog, at least not yet). Of course, this assumes people find them useful and he receives some positive feedback (and I hope he does, as video tutorials in statistics are rare).

Note that I chose not to embed the video into my blog post because I have no control over the formatting, and to ensure Creighton gets clicks to his blog post (give credit where credit is due). Click on the image to get to the video.

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