In a multiple linear regression analysis, what is the most suitable plot for checking linearity? I have seen a number of examples that use scatterplots as a preliminary test to use a linear model. But, aren't partial regression plots more reasonable?
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You don't say which scatterplots you've seen used for this -- that is, what's on the x and y axes?
However, my usual practice (and the output of R) agrees with this page from Duke University on testing linearity:
How to diagnose: nonlinearity is usually most evident in a plot of observed versus predicted values or a plot of residuals versus predicted values, which are a part of standard regression output. The points should be symmetrically distributed around a diagonal line in the former plot or around horizontal line in the latter plot, with a roughly constant variance. (The residual-versus-predicted-plot is better than the observed-versus-predicted plot for this purpose, because it eliminates the visual distraction of a sloping pattern.) Look carefully for evidence of a "bowed" pattern, indicating that the model makes systematic errors whenever it is making unusually large or small predictions. In multiple regression models, nonlinearity or nonadditivity may also be revealed by systematic patterns in plots of the residuals versus individual independent variables.
Partial regression plots don't get mentioned and I'm not sure why you think they would be better.
answered Dec 22, 2023 at 12:40