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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Allison, L. (2018). Linear Regression. In: Coding Ockham's Razor. Springer, Cham. https://doi.org/10.1007/978-3-319-76433-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-76433-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76432-0
Online ISBN: 978-3-319-76433-7
eBook Packages: Computer ScienceComputer Science (R0)