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 2022/11/29
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.