Abstract
A linear regression is a form of function-model (Chaps. 5, 8) between continuous variables. An output (dependent) variable y is approximated by a function f(x) of an input (independent) variable x with the error, y − f(x), being modelled by a model of continuous data (Chap. 4), most commonly by the Normal distribution (Sect. 4.3).
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E. Anderson, The Irises of the Gaspe Peninsula. Bull. Am. Iris Soc. 59, 2–5 (1935)
E. Anderson, The species problem in Iris. Ann. Mo. Bot. Gard. 23, 457–509 (1936). https://doi.org/10.2307/2394164
R.A. Fisher, The use of multiple measurements in taxonomic problems. Ann. Eugenics 7(II), 179–188 (1936). https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
C.S. Wallace, Multiple factor analysis by MML estimation. Technical report 218, Department of Computer Science, Monash University, 1998. http://www.allisons.org/ll/Images/People/Wallace/Multi-Factor/
C.S. Wallace, P.R. Freeman, Single-factor analysis by minimum message length estimation. J. R. Stat. Soc. Ser. B Methodol. 195–209 (1992). http://www.jstor.org/stable/2345956
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Allison, L. (2018). Linear Regression. In: Coding Ockham's Razor. Springer, Cham. https://doi.org/10.1007/978-3-319-76433-7_10
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DOI: https://doi.org/10.1007/978-3-319-76433-7_10
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