This paper will discuss the used of multiple linear regression in oil palm oil yield modeling. The foliar nutrient compositions were used as independent variable and fresh fruit bunch as dependent variable. Outliers in a set of data will influence the modeling accuracy as well as the estimated parameters especially in statistical analysis. A statistical procedure is regarded as robust if it performs reasonably well even when the assumptions of the statistical model are not true. If we assume our data follow standard linear regression model, then least squares estimates and test perform quite well, but they are not robust when the present of the outlier in the data set. In this case we are interested on M-regression to model the yield data. Since the quantile-quantile plot shows the existing of outlier, we proposed to use robust M-regression to overcome the negative impact of outlier. The data used for this study are prived by The Malaysian Oil Palm Board (MPOB) taken from two of the estates in Peninsular Malaysia. The factors included in the data set were foliar composition and fresh fruit bunches (FFB) yield. The variables in foliar composition included percentage of nitrogen concentration (N), percentage of phosphorus concentration (P), percentage of potassium concentration (K), percentage of calcium concentration (Ca) and percentage of magnesium concentration (Mg). The N, P, K, Ca and Mg concentrations were considered as independent variables and the FBB yield as dependent variable. From this analysis, it shows that robust regression gives better results than conventional regression in modeling oil palm yield.