RT Book, Section
A1 Glantz, Stanton A.
A1 Slinker, Bryan K.
A1 Neilands, Torsten B.
SR Print(0)
ID 1141898841
T1 Multicollinearity and What to Do About It
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=1141898841
RD 2024/04/20
AB Multiple  regression  allows  us  to  study  how  several  independent  variables  act  together  to  determine  the  value  of  a  dependent  variable.  The  coefficients  in  the  regression  equation  quantify  the  nature  of  these  dependencies.  Moreover,  we  can  compute  the  standard  errors  associated  with  each  of  these  regression  coefficients  to  quantify  the  precision  with  which  we  estimate  how  the  different  independent  variables  affect  the  dependent  variable.  These  standard  errors  also  permit  us  to  conduct  hypothesis  tests  about  whether  the  different  proposed  independent  variables  affect  the  dependent  variable  at  all.  The  conclusions  we  draw  from  regression  analyses  will  be  unambiguous  when  the  independent  variables  in  the  regression  equation  are  statistically  independent  of  each  other,  that  is,  when  the  value  of  one  of  the  independent  variables  does  not  depend  on  the  values  of  any  of  the  other  independent  variables.  Unfortunately,  as  we  have  already  seen  in  Chapter  3,  the  independent  variables  often  contain  at  least  some  redundant  information  and  so  tend  to  vary  together,  a  situation  called  multicollinearity.  Severe  multicollinearity  indicates  that  a  substantial  part  of  the  information  in  one  or  more  of  the  independent  variables  is  redundant,  which  makes  it  difficult  to  separate  the  effects  of  the  different  independent  variables  on  the  dependent  variable.