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 2023/09/24
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.