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162 – Celebrating 30 Years of the Survey of Consumer Finances
Reduced Major Axis Regression to Improve Oil and Gas Pipeline Integrity
William Harper
Neil A. Bates
Det Norske Veritas (Canada) Ltd.
The theoretical underpinnings of standard least squares regression analysis are based on the assumption that the independent variable (often thought of as x) is measured without error as a design variable. The dependent variable (often labeled y) is modeled as having uncertainty or error. Pipeline companies use the inline inspection (ILI) depth as the x variable and field depth as the y variable. Both measurements have multiple sources of error. Thus the underlying least squares regression assumptions are violated. Often one common result is a regression line that has a slope much less than the ideal 1-1 relationship.
Reduced Major Axis (RMA) Regression is specifically formulated to handle errors in both the x and y variables. It is not commonly found in the standard literature but has a long pedigree including the 1995 text book Biometry by Sokal and Rohlf in which it appears under the title of Model II regression. In this paper we demonstrate the potential improvements brought about by RMA regression.