TY - CHAP
M1 - Book, Section
TI - Do the Data Fit the Assumptions?
A1 - Glantz, Stanton A.
A1 - Slinker, Bryan K.
A1 - Neilands, Torsten B.
PY - 2017
T2 - Primer of Applied Regression and Analysis of Variance, 3e
AB - Up to this point we have formulated models for multiple linear regression and used these models to describe how a dependent variable depends on one or more independent variables. By defining new variables as nonlinear functions of the original variables, such as logarithms or powers, and including interaction terms in the regression equation, we have been able to account for some nonlinear relationships between the variables. By defining appropriate dummy variables, we have been able to account for shifts in the relationship between the dependent and independent variables in the presence or absence of some condition. In each case, we estimated the coefficients in the regression equation, which, in turn, could be interpreted as the sensitivity of the dependent variable to changes in the independent variables. We also could test a variety of statistical hypotheses to obtain information on whether or not different treatments affected the dependent variable. All these powerful techniques rest on the assumptions that we made at the outset concerning the population from which the observations were drawn.
SN -
PB - McGraw-Hill Education
CY - New York, NY
Y2 - 2021/05/15
UR - accessbiomedicalscience.mhmedical.com/content.aspx?aid=1141898548
ER -