In the opening episode to Season 4, Greg and Patrick delve into ordinary least squares estimation: where it came from, what it attempts to achieve, and where it can take us from here. Along the way they also discuss Golden Retrievers who are neither gold nor can retrieve, Olivia Newton John, sub-conning out their own work, the meat sweats, sh*t you should know, being intolerably self-righteous, losing your camel, saying ham in French, Calvinball, Frank finding Gauss’s corpse, crudely describing regression, and Sexy Hulk.
Lightly Edited Transcript
We provide a lightly-edited and obviously imperfect audio transcript of the episode available here. This is not an exact representation of the audio, but does provide a searchable document with identified speakers and associated time stamps.
Related Episodes
S3E17: Logistic Regression: 2 Logit 2 Quit
S3E09: Semi-Partially Clarifying Measures of Association in Regression
S2E30: ‘Always Center Your Predictors!’ And Other Sh*t My Advisor Says
S2E18: Regression — Like That Old High School Friend You’ve Outgrown
S2E14: Control (Variable) Issues
Suggested Readings
Dismuke, C., & Lindrooth, R. (2006). Ordinary least squares. Methods and Designs for Outcomes Research, 93, 93-104.
Hansen, B. E. (2022). A Modern Gauss–Markov Theorem. Econometrica, 90(3), 1283-1294.
Kruskal, W. (1968). When are Gauss-Markov and least squares estimators identical? A coordinate-free approach. The Annals of Mathematical Statistics, 39(1), 70-75.
Lakshmi, K., Mahaboob, B., Rajaiah, M., & Narayana, C. (2021). Ordinary least squares estimation of parameters of linear model. J. Math. Comput. Sci., 11(2), 2015-2030.
Puntanen, S., & Styan, G. P. (1989). The equality of the ordinary least squares estimator and the best linear unbiased estimator. The American Statistician, 43(3), 153-161.