Summary: In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables. Although there are various methods for estimating parameters, the most popular is the Ordinary Least Squares (OLS) method. However, in the presence of multicollinearity and outliers, the OLS estimator may give inaccurate values and also misleading inference results. There are many modified biased robust estimators for the simultaneous occurrence of outliers and multicollinearity in the data. In this paper, a new estimator called the Liu-Ratio Estimator (LRE), which can be used as an alternative to the Least Squares Ratio (LSR) estimator and the Ridge Ratio estimator (RRE), is proposed to mitigate the effect of \(y\)-direction outliers and multicollinearity in the data. The performance of the proposed estimator is examined in two Monte Carlo simulation studies in the presence of multicollinearity and \(y\)-direction outliers. According to the simulation results, LRE is a strong alternative to LSR and RRE in the presence of multicollinearity and \(y\)-direction outliers in the data.