Outliers in a Linear Regression Model

Over the last several decades the linear regression model has become one of the most widely used tools of the social sciences and the physical sciences. Given the data, the least squares method gives information for statistical inferences. However, the researcher frequently feels that the regression results are not trustworthy because of possible problems with the data. These problems have sometimes been ignored in practice. It is absurd that we include all data without question if some of the data are in error, or they come from a different regime. Those data are called outliers and should be excluded from the sample or at least treated carefully.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Economics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Miyashita, Hiroshi

• Statistics

Identifier

(UMI)AAI8218524

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/70725