TY - CHAP
M1 - Book, Section
TI - Regression with Two or More Independent Variables
A1 - Glantz, Stanton A.
A1 - Slinker, Bryan K.
A1 - Neilands, Torsten B.
Y1 - 2017
N1 -
T2 - Primer of Applied Regression and Analysis of Variance, 3e
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.
SN -
PB - McGraw-Hill Education
CY - New York, NY
Y2 - 2023/05/30
UR - accessbiomedicalscience.mhmedical.com/content.aspx?aid=1141898187
ER -