July 2012
Intermediate to advanced
352 pages
9h 30m
English
book
Common Errors in Statistics (and How to Avoid Them), 4th Edition
by Phillip I. Good, James W. Hardin
Content preview from Common Errors in Statistics (and How to Avoid Them), 4th Edition
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LINEAR VERSUS NONLINEAR REGRESSION
Linear regression is a much misunderstood and mistaught concept. If a linear model provides a good fit to data, this does not imply that a plot of the dependent variable with respect to the predictor would be a straight line, only that a plot of the dependent variable with respect to some not-necessarily monotonic function of the predictor would be a line.
For example, y = A + B log[x] and y = A cos(x) + B sin(x) are both linear models whose coefficients A and B might be derived by OLS or LAD methods. Y = Ax5 is a linear model. Y = xA is nonlinear.
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