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Model Selection with Stepwise Regression

While no model selection criteria is perfect, making use of the avaliable statistical tools allows a researcher to systematically choose a set of predictive variables for a given set of data and critiria. The selection process can be done by an autmoatic procedure in the form of a sequence of tests such as F-tests or making use of the Akaike information criterion.

"The most that can be expected from any model is that it can supply a useful approximation to reality: All models are wrong; some models are useful". -- George Box

Requirements

  • R: environment for statistical computing and graphics
  • car: Companion to Applied Regression
  • gplot: Various R Programming Tools for Plotting Data

References

BibTeX

@article{Higinbotham:2015rja,
      author         = "Higinbotham, Douglas W. and Kabir, Al Amin and Lin,
                        Vincent and Meekins, David and Norum, Blaine and Sawatzky,
                        Brad",
      title          = "{Proton Radius from Electron Scattering Data}",
      journal        = "Phys. Rev.",
      volume         = "C93",
      year           = "2016",
      number         = "5",
      pages          = "055207",
      doi            = "10.1103/PhysRevC.93.055207",
      eprint         = "1510.01293",
      archivePrefix  = "arXiv",
      primaryClass   = "nucl-ex",
      reportNumber   = "JLAB-PHY-16-2",
      SLACcitation   = "%%CITATION = ARXIV:1510.01293;%%"
}
@Manual{R,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2013},
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org/},
}
@Book{car,
title = {An {R} Companion to Applied Regression},
edition = {Second},
author = {John Fox and Sanford Weisberg},
year = {2011},
publisher = {Sage},
address = {Thousand Oaks {CA}},
url = {http://socserv.socsci.mcmaster.ca/jfox/Books/Companion},
}