Detection of Outliers in Regression Model for Medical Data
Journal Title: International Journal of Medical Research & Health Sciences (IJMRHS) - Year 2017, Vol 6, Issue 7
Abstract
In regression analysis, an outlier is an observation for which the residual is large in magnitude compared to other observations in the data set. The detection of outliers and influential points is an important step of the regression analysis. Outlier detection methods have been used to detect and remove anomalous values from data. In this paper, we detect the presence of outliers in simple linear regression models for medical data set. Chatterjee and Hadi mentioned that the ordinary residuals are not appropriate for diagnostic purposes; a transformed version of them is preferable. First, we investigate the presence of outliers based on existing procedures of residuals and standardized residuals. Next, we have used the new approach of standardized scores for detecting outliers without the use of predicted values. The performance of the new approach was verifed with the real-life data.
Authors and Affiliations
Stephen Raj S| Research Scholar, Department of Statistics, Manonmaniam Sundaranar University, Abhishekapatti, Tirunelveli, Tamil Nadu, India Corresponding e-mail: stephenstats17@gmail.com, Senthamarai Kannan K| Professor, Department of Statistics, Manonmaniam Sundaranar University, Abhishekapatti, Tirunelveli, Tamil Nadu, India
Keywords
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