Two families of statistical models of increasing statistical complexity are presented which generalize global confinement expressions to plasma profiles and local transport coefficients. The temperature or diffusivity is parameterized as a function of the normalized flux radius,ψ, and the engineering variables, u = (I p , B t ,n, q 95 ) † . The log-additive temperature model assumes that ln [T (ψ, u). The unknown f i (ψ) are estimated using smoothing splines. The Rice selection criterion is used to determine which terms in the log-linear model to include. A 43 profile Ohmic data set from the Joint European Torus [P. H. Rebut, et al., Nuclear Fusion 25 1011] is analyzed and its shape dependencies are described. The best fit has an average error of 152 eV which is 10.5 % percent of the typical line average temperature. The average error is less than the estimated measurement error bars. The second class of models is log-additive diffusivity models where ln[χ(ψ, u)] = g 0 (ψ) + g I (ψ) ln[I p ] +g B (ψ) ln[B t ] +g n (ψ) ln[n]. These log-additive diffusivity models are useful when the diffusivity is varied smoothly with the plasma parameters. A penalized nonlinear regression technique is recommended to estimate the g i (ψ). The physics implications of the two classes of models, additive log-temperature models and additive log-diffusivity models, are different. The additive log-diffusivity models adjust the temperature profile shape as the radial distribution of sinks and sources. In contrast, the additive log-temperature model predicts that the temperature profile depends only on the global parameters and not on the radial heat deposition.