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Lack of fit - significant R square in two "almost similar x/y models"


Occasional Contributor


May 4, 2017



I know a similar question was asked before, but please help me to apply this to my model:


1. test two regressions - A versus B and A versus C (similar observations n=1242)

2. Model A versus B has an higher Rsquare and seems to have a better correlation - but a Lack of fit < 0.05

3. Model A versus C has a lower Rsquare and sems to have a bit less correlation - but a lack of fit > 0.05

4. The residual plot looks quite similar.

 For the actual full result please see also my attached PDF.


My question: Do I have to disregard Model A versus B and conclude that A versus C is better and more appropriate for my analysis ?

Screenshot (11).png






Super User


Jul 13, 2011

Based on a brief glance of the models my feeling is that you are worrying too much about the detailed statistics.  The 2 models look almost identical to me (look at the bivariate plots not just the stats) - clearly there is a very high level of correlation between B and C. Does your scientific understanding favour one model over another?

What stands out to me are the high leverage points at a value of about 70 (for both B and C - strange that they are both on the same scale - are they different measures of the same thing?).  Anyhow, I would be concerned about the degree of leverage of those points and their overall influence on the regression.


Occasional Contributor


May 4, 2017

Many thanks David.
All are physiological variables, all measurements are done with the same method - hence trying to find the best predictor for A. B and C are dependant to each other.
I will explore the outliers separately in a later, but before this I wanted to configure the best model to define outliers (>2SD or >90% percentile) - although they maybe the same in each model anyway (I didn't check this yet).
Thanks a lot, Marc