# user1447630

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bio website location age member for 1 year, 6 months seen Oct 18 '12 at 13:17 profile views 95

# 34 Actions

 Oct22 awarded Popular Question Jun10 awarded Yearling Oct18 accepted Is it possible to have a variable that acts as both an effect modifier and a confounder? Oct5 awarded Nice Question Sep30 revised Is it possible to have a variable that acts as both an effect modifier and a confounder? added 46 characters in body Sep30 asked Is it possible to have a variable that acts as both an effect modifier and a confounder? Sep10 suggested suggested edit on How do I test a nonlinear association? Sep10 comment How do I test a nonlinear association? @Macro and Michael to me fitting a model of the relation between $x$ and $y$ in a semi/non-parametric way is one way of testing the association between the two. Such a test could be extended by measuring the extent of association with the different ways you've each suggested. I think both answers and the follow-up here have been quite useful to me, sans the ad hominem. However, since my question did include how we could "label its nature", which could be interpreted as model-fitting, I'm going to stick with Macro's answer. Sep9 accepted Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points Sep9 awarded Commentator Sep9 comment Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points Is there some way of implementing a 2D KS test (any variation) in R? Package? Sep9 accepted How do I test a nonlinear association? Sep9 comment How do I test a nonlinear association? I prefer this approach to the two separate rank correlations either side of $x=a$ because it examines the relation as whole. It's also better than the parametric model, so I've accepted this instead. Sep8 awarded Supporter Sep8 comment How do I test a nonlinear association? Essentially, I'd be splitting the x~y relation into two parts. Below x=a, the correlation by Spearman's rho is positive. Above x=a, the correlation by Spearman's rho is negative. I like this approach. However, is there also some way of parametrically testing whether the relationship between x and y fits an inverse parabola, i.e. $y = ax^2 + bx + c$, where $a$ is negative. Perhaps, this requires a custom statistical test? Sep8 comment Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points @MichaelChernick this question is independent of my previous post although that doesn't discount the possibility of overlap in answers. Sep8 revised Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points added 308 characters in body Sep8 comment Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points @MichaelChernick that's correct. I want to compare two curves based on two-dimensional points. However, I've rephrased my question to reflect the fact that basically I want to know whether the distribution of the (x,y) coordinates of the blue points is significantly different from the distribution of the (x,y) coordinates of the red points. I'd also want to know the directionality of this difference. Sep7 asked Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points Sep7 asked How do I test a nonlinear association?