I'm trying to figure out which nonparametric test I should run on my data.
My data has residuals that are not normal, so I cannot run a linear regression unless I log transform it. However, log transforming my data would make it difficult to interpret a quadratic model, so I need to run a nonparametric test similar to regression.
I'm trying to compare 2 models and determine which has a better fit - a linear model (y~x) or a quadratic model (y~x+x^2). Which nonparametric approach similar to regression should I use to construct each model?