Nodal few-group cross-section representation models are used to generate estimates of cross-section parameters for nodal codes as a function of various state parameters. In this work we present a feasibility study for constructing representational models which provide mean and covariance estimates of these parameters. We consider two sources of uncertainty in the model predictions, namely the uncertainty due to randomized nuclear data during the lattice calculation and the uncertainty in the model parameters. To handle these uncertainties, we investigate the use of parametric polynomial regression models and parametric Bayesian polynomial regression models. These methods are tested on a typical training set of cross-section parameters for pressurized water reactor analysis.