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- W4244611114 abstract "The use of variable selection methods can be very helpful when there are many potential regressors, but selecting regressors when there is multicollinearity can be difficult. This chapter discusses the various methods used for selecting a subset of regression variables. The methods such as forward selection and backward elimination can perform very poorly relative to criteria such as R2. Variable selection techniques are usually discussed relative to the selection of linear terms, but they may also be used to detect the need for nonlinear and interaction terms. Once a subset model has been selected, it is advisable to collect new data, if possible, to validate the model before using it. The chapter illustrates stepwise regression to the MINITAB tree data. S-Plus performs stepwise regression in a rather unusual way, with the step function using the value of the Cp statistic as the criterion, rather than performing hypothesis tests." @default.
- W4244611114 created "2022-05-12" @default.
- W4244611114 date "2008-12-01" @default.
- W4244611114 modified "2023-10-12" @default.
- W4244611114 title "Selection of Regressors" @default.
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- W4244611114 doi "https://doi.org/10.1002/9780470382806.ch7" @default.
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