Tobit regression is a statistical model used to estimate linear relationships when the dependent variable is censored or truncated. It is specifically designed for situations where the observed dependent variable is limited (e.g., at zero) for a subset of observations, while an underlying continuous latent variable is assumed to exist, providing consistent and efficient parameter estimates in contexts where standard ordinary least squares (OLS) would produce biased results.