In regression on 13 November 2008 at 5:16 pm
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.
In mixed on 27 May 2008 at 12:45 am
The National Bureau of Economic Research—a private, nonprofit, nonpartisan research organization—has made public an eighteen-hour workshop from it’s Summer Institute 2007: What’s New in Econometrics? Included are lecture videos, notes, and slides from the series.
The lectures cover recent advances in econometrics and statistics. The topics include (in the order presented):
- Estimation of Average Treatment Effects Under Unconfoundedness
- Linear Panel Data Models
- Regression Discontinuity Designs
- Nonlinear Panel Data Models
- Instrumental Variables with Treatment Effect Heterogeneity: Local Average Treatment Effects
- Control Function and Related Methods
- Bayesian Inference
- Cluster and Stratified Sampling
- Partial Identification
- Difference-in-Differences Estimation
- Discrete Choice Models
- Missing Data
- Weak Instruments and Many Instruments
- Quantile Methods
- Generalized Method of Moments and Empirical Likelihood
The speakers explain the material well, including some practical pros and cons to the methods presented. The slides are, however, typically academic: packed with content and equations, with little to support the speaker. In a way it’s expected, but surprising given that lecture notes are provided.
It takes a bit of time to get into the talks, but once you do there’s lots to learn. I suggest two open browser windows: one for the videos, one for the slides. But avoid the temptation to read the slides—the speakers explain the material well and you’ll pick up quite a bit if you can focus on what they’re saying while you stare lovingly at the equations.
Special thanks to John Graves at the Social Science Statistics Blog for posting a notice about the series.