Yeadon M R, Morlock M
Biomechanics Laboratory, Faculty of Physical Education, University of Calgary, Alberta, Canada.
J Biomech. 1989;22(6-7):683-9. doi: 10.1016/0021-9290(89)90018-3.
Linear regression equations are commonly used in conjunction with experimental data to provide linear relationships between quantities which are dimensionally distinct. In many cases theoretical relationships between such quantities are known and can be used as a basis for non-linear regression equations. This study compares linear and non-linear approaches for estimating the segmental moments of inertia from anthropometric measurements using the data of Chandler et al. [Chandler et al. (1975) Investigation of inertial properties of the human body. AMRL Technical Report 74-137, Wright Patterson Air Force Base. OH.] Right limb data were used to derive the equations while left limb data were used as a cross-validation sample to evaluate the inertia estimates calculated from the equations. For the limb segments the standard error estimates had average values of 21% for the linear equations and 13% for the non-linear equations. Data on a 10 yr-old boy was used to compare the two approaches outside the sample range. The mean percentage residuals were 286% for the linear equations and 20% for the non-linear equations. A set of non-linear equations is provided.
线性回归方程通常与实验数据结合使用，以提供维度不同的量之间的线性关系。在许多情况下，这些量之间的理论关系是已知的，可作为非线性回归方程的基础。本研究使用钱德勒等人的数据，比较了从人体测量数据估计节段转动惯量的线性和非线性方法[钱德勒等人（1975年）人体惯性特性研究。AMRL技术报告74 - 137，赖特·帕特森空军基地，俄亥俄州]。用右肢数据推导方程，而用左肢数据作为交叉验证样本，以评估根据方程计算出的惯性估计值。对于肢体节段，线性方程的标准误差估计平均值为21%，非线性方程为13%。使用一名10岁男孩的数据在样本范围外比较这两种方法。线性方程的平均百分比残差为286%，非线性方程为20%。提供了一组非线性方程。
