In this paper, we propose a unit distribution called the Logit Gudermannian distribution and present various statistical properties of the proposed model. Six parameter estimation methods are explored in the quest to estimate the parameters of the proposed distribution. We determine which estimation methods provide better parameter estimates through simulation studies. The study shows that the Logit Gudermannian distribution provides a better fit for the datasets used than other unit distributions. Consequently, the Logit Gudermannian distribution is used to develop a parametric regression model for studying the relationship between a unit response variable and other exogenous variables. The new regression model's performance is compared to that of other existing regression models and found to be competitive.