Previously, we have introduced an approach for calculation of the full object distance in the frame of Principal Component Analysis that can be applied to data exploration and classification. Now, a similar approach has been developed for regression problems in which a total distance can be calculated for every sample in projection modeling. Based on the total distance, a threshold for outlier detection has been developed by means of a data-driven estimation of the degrees of freedom and scaling parameters for the partial distances in the projection models. A joint threshold is used as a basis for a sequential outlier detection procedure. The iterative nature of the procedure helps to overcome masking effect in outliers, and a backward step eliminates swamping effects. Two real examples are used for illustration. The first dataset represents capsules filled with specially prepared mixtures of an active pharmaceutical ingredient and a number of excipients. This dataset is used to illustrate the behavior of possible outliers in the regression model and their corresponding locations in the X- and XY-distance plots. The second dataset consists of spectra of 135 whole wheat samples used for the prediction of protein, gluten, and moisture content. This dataset is used for a demonstration of the step-by-step application of the sequential procedure for outlier detection.