Week 2 Revisiting Flatland
A review of general linear models
Description
In Edwin Abbott’s 1884 novella, the inhabitants of Flatland are geometric shapes living in a two-dimensional world, incapable of imagining the existence of higher dimensions. A sphere passing through the plain of their world is a fascinating but incomprehensible event: Flatlanders can only see a dot becoming a circle, increasing in circumference, then shrinking back in size and disappearing. There are, in this universe, worlds with even more limited views, like the one-dimensional Lineland and the zero-dimensional Pointland. Any attempt to expand the perspective of their inhabitant(s) is doomed to failure. But as in any good adventure story, a chosen Flatland native embarks on a journey of discovery and revelation - and ostracism and imprisonment. The story is interpreted as an allegorical criticism of Victorian-age social structure, but can equally describe the limitations of inhabiting uncritically a methodological world in which all data are ‘normal’ and all relationships are linear. Moving beyond linearity and acquiring the statistical intuition needed to think in higher dimensions and perceive more complex relationships is indeed a matter of practice-induced revelation. It’s unlikely that we will reach statistical nirvana in this short course, but we’ll attempt to build some more substantial structures upon the arid plains of linear regression. We start by looking around in the Flat-, Line- and Point-lands of quantitative analysis. Incorrigible procrastinators may want to check out a full-length computer animated film version of Flatland on YouTube. Others may be better served by this brief TED-Ed animation.
Readings
Statistics
Coding
Application
- Österman, Marcus. 2021. ‘Can We Trust Education for Fostering Trust? Quasi-Experimental Evidence on the Effect of Education and Tracking on Social Trust’. Social Indicators Research 154(1):211–33 - (online)
References
David, F. N. 1955. “Studies in the History of Probability and Statistics i. Dicing and Gaming (a Note on the History of Probability).” Biometrika 42 (1/2): 1–15. https://doi.org/10.2307/2333419.
El-Shagi, Makram, and Alexander Jung. 2015. “Have Minutes Helped Markets to Predict the MPC’s Monetary Policy Decisions?” European Journal of Political Economy 39 (September): 222–34. https://doi.org/10.1016/j.ejpoleco.2015.05.004.
Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2020. Regression and other stories. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781139161879.
Lord, R. D. 1958. “Studies in the History of Probability and Statistics.: VIII. De Morgan and the Statistical Study of Literary Style.” Biometrika 45 (1/2): 282–82. https://doi.org/10.2307/2333072.
McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Second. CRC Texts in Statistical Science. Boca Raton: Taylor and Francis, CRC Press.
Mulvin, Dylan. 2021. Proxies: The Cultural Work of Standing in. Infrastructures Series. Cambridge, Massachusetts: The MIT Press.
Senn, Stephen. 2003. “A Conversation with John Nelder.” Statistical Science 18 (1): 118–31. https://doi.org/10.1214/ss/1056397489.