Moving beyond linearity, this chapter examines deterministic and stochastic nonlinear models. We formalize inference under nonlinear least squares, emphasize the distinction between intrinsic and extrinsic curvature, and explore identifiability issues. Practical considerations—starting-value sensitivity, convergence diagnostics, and interval estimation via the delta method.