The objective of this work is to develop statistical models to predict Locked Wheel Skid Trailer (LWST) skidnumbers from Dynamic Friction Tester (DFT) and Circular Texture Meter (CTM) measurements conductedon asphalt pavement surfaces. The analyses conducted are descriptive as well as analytical. They include alldescriptive measures along with linear and nonlinear regressions. For both analyses, DFT measurements at 20km/h (12.5 mph) and 64 km/h (40 mph) and Mean Profile Depth (MPD) were used to predict LWST skidvalues. Furthermore, the International Friction Index (IFI) parameters (F60 and SP) were used in an additionalanalysis to predict LWST skid values. Multiple linear regression techniques were used to identify thesignificant quantitative predictors. Model selection using stepwise regression showed that DFT64 and MPDare statistically significant predictors. Moreover, regression analysis showed that DFT20 is highly correlatedwith other predictors and therefore removed due to multicollinearity. Additionally, it was shown that F60 andSP are also significant in predicting the dependent variable with slightly less correlation coefficients.Nonlinear regression technique was also utilized for the same purpose. In both cases, higher correlationcoefficients were noticed when using the nonlinear method as opposed to the multiple linear regressionmethod.