Analysis of variance and linear models is undoubtedly one of the most useful statistical contributions to experimental and observational science. With the ability to characterize a system through multivariate responses, these methods have emerged to be general tools regardless of response dimensionality. Contemporary methods for establishing statistical inference, such as ANOVA simultaneous component analysis (ASCA), are based on Monte Carlo sampling; however, a flat uniform resampling scheme may violate the structure of the uncertainty for unbalanced designs as well as for observational data. In this work, we provide permutation strategies for inferential testing for unbalanced designs including interaction models and establish nonuniform randomization based on the concept of propensity score matching. Lastly, we provide a general method for modelling continuous covariates based on kernel smoothers. All methods are characterized on their ability to provide unbiased Type I error results.