Modeling Considerations in Wood-Related Research

    Volume 27, Issue 1 (January 1999)

    ISSN: 0090-3973

    CODEN: JTEOAD

    Page Count: 6


    Pellicane, PJ
    Professor of Wood Engineering, Colorado State University, Fort Collins, CO

    (Received 24 June 1997; accepted 17 July 1998)

    Abstract

    It has been the author's experience that researchers will routinely use linear models to describe the relationships between correlated variables when that approach to modeling may not be the most rational. This article illustrates some of the circumstances when linear modeling is and is not the most viable alternative in characterizing the relationship between correlated variables.

    In this study, several data sets are examined to illustrate how amenable they are to linear modeling. One data set selected shows a nearly ideal example of when linear least-squares regression is a realistic descriptor of the relationship between correlated variables. A second data set shows how material idiosyncrasies, such as species, size, or grade effects, can result in misleading models for parameter prediction. A third data set illustrates a more subtle inhomogeneity that is frequently found in experimental data involving tests of clear wood. When the relationship between correlated variables changes due to a shifting failure mechanism, a presumed linear relationship may misrepresent the relationship between variables in a large portion of the domain. It is the intent of this paper to remind or make researchers aware of the subtle characteristics of data sets that can influence modeling results.

    One possible mechanism for modeling nonlinearly related variables (the univariate SB and the bivariate SBB distribution) is offered for consideration. This model has the unique feature of quantifying, in an analytic, closed-form fashion, the probability of a predicted variable for any value of the domain. In addition, when certain input values used to calculate SBB bivariate distribution parameters are defined correctly, convergence to the clear wood strength of a material or product is achieved.


    Paper ID: JTE12041J

    DOI: 10.1520/JTE12041J

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    Author
    Title Modeling Considerations in Wood-Related Research
    Symposium , 0000-00-00
    Committee D07