Abstract
Frequently we start out with a fairly long list of independent variables that we suspect have some effect on the dependent variable, but for various reasons we would like to cull the list. One important reason is the resultant parsimony: It is easier to work with simpler models. Another is that reducing the number of variables often reduces multicollinearity. Still another reason is that it lowers the ratio of the number of variables to the number of observations, which is beneficial in many ways.
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© 1990 Springer-Verlag New York Inc.
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Sen, A., Srivastava, M. (1990). Variable Selection. In: Regression Analysis. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4470-7_11
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DOI: https://doi.org/10.1007/978-1-4612-4470-7_11
Publisher Name: Springer, New York, NY
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