RT Book, Section
A1 Glantz, Stanton A.
A1 Slinker, Bryan K.
A1 Neilands, Torsten B.
SR Print(0)
ID 1141899067
T1 Selecting the “Best” Regression Model
T2 Primer of Applied Regression and Analysis of Variance, 3e
YR 2017
FD 2017
PB McGraw-Hill Education
PP New York, NY
SN 9780071824118
LK accessbiomedicalscience.mhmedical.com/content.aspx?aid=1141899067
RD 2022/01/22
AB Our discussion of regression analysis to this point has been based on the premise that we have correctly identified all the relevant independent variables. Given these independent variables, we have concentrated on investigating whether it was necessary to transform these variables or to consider interaction terms, evaluate data points for undue influence (in Chapter 4), or resolve ambiguities arising out of the fact that some of the variables contained redundant information (in Chapter 5). It turns out that, in addition to such analyses of data using a predefined model, multiple regression analysis can be used as a tool to screen potential independent variables to select that subset of them that make up the “best” regression model. As a general principle, we wish to identify the simplest model with the smallest number of independent variables that will describe the data adequately.