TY - CHAP
M1 - Book, Section
TI - Selecting the “Best” Regression Model
A1 - Glantz, Stanton A.
A1 - Slinker, Bryan K.
A1 - Neilands, Torsten B.
PY - 2017
T2 - Primer of Applied Regression and Analysis of Variance, 3e
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.
SN -
PB - McGraw-Hill Education
CY - New York, NY
Y2 - 2022/01/22
UR - accessbiomedicalscience.mhmedical.com/content.aspx?aid=1141899067
ER -