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ERIC Number: ED427059
Record Type: Non-Journal
Publication Date: 1999-Jan
Pages: 40
Abstractor: N/A
Reference Count: N/A
Strategies for Detecting Outliers in Regression Analysis: An Introductory Primer.
Evans, Victoria P.
Outliers are extreme data points that have the potential to influence statistical analyses. Outlier identification is important to researchers using regression analysis because outliers can influence the model used to such an extent that they seriously distort the conclusions drawn from the data. The effects of outliers on regression analysis are discussed, and examples of various detection methods are given. Most outlier detection methods involve the calculation of residuals. Given that the identification of a point as an outlier is not, in itself, grounds for exclusion, the questions that must be answered is when an outlying observation can be rejected legitimately. When individuals admit inattention during data collection, or acknowledge providing dishonest responses, the decision to delete outliers is straightforward. It is only troubling to delete them when the basis for the aberrance cannot be understood, and then the decision is the most difficult. Three appendixes contain a FORTRAN program to compute a type of detection matrix, input for that program, and output results for the example data. (Contains 4 tables, 6 figures, and 11 references.) (SLD)
Publication Type: Reports - Descriptive; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A