One of the major difficulties when comparing the allometric scaling coefficient (b), which is the slope of the power function, Y = aXb, or more conveniently, log Y = log a + b log X, from different studies is axis rotation. Using the example of the relationship between tree height and diameter, there is no standard for assigning a variable to a particular axis, as neither height nor diameter are independent variables. Both of these variables are dependent on tree age, but are usually highly correlated with each other. The slope of the relationship between the two variables, can be calculated using ordinary least squares, reduced major axis or geometric mean regression. However, with ordinary least squares regression, when the axes are rotated, the new slope estimate is not the inverse of the original slope estimate before rotation. The resulting error can be large and is negatively correlated to the absolute value of the correlation coefficient (r). Only with reduced major axis or geometric mean regression is it possible to obtain the inverse relationship with a high degree of accuracy by simply taking the inverse of the slope. With reduced major axis regression, the allometric scaling coefficient is determined by dividing the slope obtained by ordinary least squares regression by r. Therefore, the allometric scaling coefficient can be calculated if authors also include along with the slope, either the correlation coefficient (r) or the coefficient of determination (r2). The allometric scaling coefficient can also be calculated using the geometric mean technique as long as authors include the slopes of both X versus Y and Y versus X. It is imperative that authors state the type of regression used if meaningful comparisons among studies are to be made.

Key words: allometry, axis rotation, regression