Least Absolute Deviation Regression and Least Squares for Modeling Retention Indices of Set Compounds Food and Pollutants of the Environment

Considering the importance of the statistical analysis of regression in modeling based separately on study for Quantitative structure retention indices on Carbowax 20 M (I^Cw20M) and OV-101 columns (I^OV-101) relationships (QSRR) are determined for 114 pyrazines. The detection of influential observations for the standard least squares regression model is a problem which has been extensively studied. Least Absolute Deviation regression diagnostics offers alternative dicapproaches whose main feature is the robustness. Here a nonparametric method for detecting influential observations is presented and compared with other classical diagnostics methods. With have been applied for modeling separately retention indices of the same set of (89 pyrazines of Training and 25 of Test) eluted on Columns OV-101 and Carbowax-20M, using theoretical molecular descriptors derived from DRAGON Software and validating the results in the state approached graphically by Probability plot of the error and approached tests statistics of Anderson-Darling, in finished by the confidence interval thanks to robustness concept to check if errors distribution is really approximate.