DEALING WITH OUTLIERS AND INFLUENTIAL POINTS WHILE FITTING REGRESSION

Abstract

Dealing with outliers and influential points while fitting regression is recognizing them, identifying the reasons to their existence in the process and employing the best alternatives to lessen their effect to the fitted regression model. In this paper, before considering elimination of outliers and the influential points while fitting a regression, as they contain important information, issues why unusual observations (possible outliers) appear in the process and how to analyze them to detect if they were real outliers, have been discussed thoroughly. And, when detected as outliers and influential points, to investigate and eliminate their effect in the fitted model, analytic procedures; leverage value, studentized residuals and cook's distance were carefully employed to optimize a multiple regression model for rice production forecasting in Nepal. This model was fitted with 35 years (1961-1995) time series data, collected from Ministry of Agriculture and Cooperatives, Food and Agriculture Organization Statistics Database, International Rice Research Institute and Department of Hydrology and Metrology which to its end was consisted of the three predictors, price at harvest, rural population and area harvested.Journal of Institute of Science and TechnologyVolume 22, Issue 1, July 2017, Page: 61-65