search for: loss_of_significance

> I think what you're seeing is > https://en.wikipedia.org/wiki/Loss_of_significance. Almost. All the results in the OP's summary are reflections of finite precision in the analytically exact solution, leading to residuals smaller than the double precision limit. The summary is correctly warning that it's all potentially nonsense, and indeed the only things you can trust...

...stic = NaN, p-value = NA, R-squared = NaN, etc. All of which is what ought to happen, given that the response variable (y) is not actually variable. ---JRG John R. Gleason On 09/06/2017 09:10 AM, S Ellison wrote: >> I think what you're seeing is >> https://en.wikipedia.org/wiki/Loss_of_significance. > > Almost. > All the results in the OP's summary are reflections of finite precision in the analytically exact solution, leading to residuals smaller than the double precision limit. The summary is correctly warning that it's all potentially nonsense, and indeed the only things...

Tim, I think what you're seeing is https://en.wikipedia.org/wiki/Loss_of_significance. Cheers, Mark From: "Glover, Tim" <Tim.Glover at amecfw.com> To: "r-help at r-project.org" <r-help at r-project.org> Date: 09/05/2017 11:37 AM Subject: [R] Interesting behavior of lm() with small, problematic data sets Sent by: "R-hel...

I've recently come across the following results reported from the lm() function when applied to a particular type of admittedly difficult data. When working with small data sets (for instance 3 points) with the same response for different predicting variable, the resulting slope estimate is a reasonable approximation of the expected 0.0, but the p-value of that slope estimate is a surprising