Open AccessDetecting Outliers In Multiple Linear Regression
Author(s) -
إيهاب عبد السلام محمود
Publication year - 2011
Publication title -
mağallaẗ al-ʿulūm al-iqtiṣādiyyaẗ wa-al-idāriyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2518-5764
pISSN - 2227-703X
DOI - 10.33095/jeas.v17i64.900
Subject(s) - outlier , linear regression , regression , statistics , regression analysis , anomaly detection , computer science , robust regression , linear model , data mining , mathematics , artificial intelligence , econometrics
It is well-known that the existence of outliers in the data will adversely affect the efficiency of estimation and results of the current study. In this paper four methods will be studied to detect outliers for the multiple linear regression model in two cases : first, in real data; and secondly, after adding the outliers to data and the attempt to detect it. The study is conducted for samples with different sizes, and uses three measures for comparing between these methods . These three measures are : the mask, dumping and standard error of the estimate.