Linear Regression to a Lower Order Model: Effects and Implications
NAVAL UNDERWATER SYSTEMS CENTER NEWPORTRI
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Linear regression is used extensively in the fields of science, engineering, and business. Because data-gathering processes can be complex, show random effects, or be unknown, often times the regression model used only approximates the actual process model. This report analyzes the effects of using a reduced-order process model in linear regression. In particular, the relationship of the Taylor series coefficients to the regression parameters is discussed. Relationships are generated to equate regression and Taylor series parameters, and an error analysis is performed to compare the effects of noise and modeling errors.
- Statistics and Probability