RT Book, Section
A1 Glantz, Stanton A.
A1 Slinker, Bryan K.
A1 Neilands, Torsten B.
SR Print(0)
ID 1141898187
T1 Regression with Two or More Independent Variables
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=1141898187
RD 2025/01/18
AB Chapter  2  laid  the  foundation  for  our  study  of  multiple  linear  regression  by  showing  how  to  fit  a  straight  line  through  a  set  of  data  points  to  describe  the  relationship  between  a  dependent  variable  and  a  single  independent  variable.  All  this  effort  may  have  seemed  somewhat  anticlimactic  after  all  the  arguments  in  Chapter  1  about  how,  in  many  analyses,  it  was  important  to  consider  the  simultaneous  effects  of  several  independent  variables  on  a  dependent  variable.  We  now  extend  the  ideas  of  simple  (one  independent  variable)  linear  regression  to  multiple  linear  regression,  when  there  are  several  independent  variables.  The  ability  of  a  multiple  regression  analysis  to  quantify  the  relative  importance  of  several  (sometimes  competing)  possible  independent  variables  makes  multiple  regression  analysis  a  powerful  tool  for  understanding  complicated  problems,  such  as  those  that  commonly  arise  in  biology,  medicine,  and  the  health  sciences.