Pakistan Journal of Social Sciences
ISSN: Online 1993-6052ISSN: Print 1683-8831
Abstract
Five detection of outliers procedures in Multiple regression model are looked into, compared and investigated with a simulated data. The researchers reviewed five outlier detection methods in multiple linear regression model and then compares theirs results by using two criteria of robust diagnostics called the Median Absolute Deviation (MAD) and the Standard Deviation (SD) parameter estimate. Data were generated with 10, 20 and 30% of outliers on X1’s, X2’s and both X1’s and X2’s, respectively with different sample sizes (20, 50 and 1 2 1 2 100) to check and compare outliers in the residual space of CovRatio which will flag observations that are influential because of large residual, outliers in the X-space of Hat Diagonal which flags observations that is influential because they are outliers in the X-space, the Dffits shows the influence on fitted values and measures the impact on the regression coefficients. Cook’s D measures the overall impact that a single observation has on the regression coefficient estimates and Mahalanobis Distance measures the hat leverage through the means of Mdi.
How to cite this article:
Azeez Adeboye, Ndege James and Odeyemi Akinwumi. Assessment of Outlier Detection Procedures in Analysis of Regression Model.
DOI: https://doi.org/10.36478/pjssci.2016.25.31
URL: https://www.makhillpublications.co/view-article/1683-8831/pjssci.2016.25.31