| （台州学院 体育科学学院，浙江 临海 317000） 摘 要：OLS逐步回归等变量删除法在处理运动训练领域的共线性数据时，会将一些重要的解释变量或样本点排除在模型之外，从而削弱了理论的优先地位和导向功能。PCR模型对系统信息的综合与筛选能力不佳，拟合与预测精度不甚理想。PLS对系统信息的综合与筛选能力强、拟合与预测精度较高，是目前处理运动训练领域小样本多指标共线性数据的一个非常有效的统计方法，并为运动训练学理论模型的实证检验提供了可能。其不足之处在于较难明确辨识所提取成分的物理含义。 |
MA Yong-zhan
| （Department of Physical Education，Taizhou University，Linhai 317000，China） Abstract: When such variable deletion methods as OLS stepwise regression are used to process collinear data in the sports training area, they will reject some important explanatory variables or sample points out of the models, thus weakening the theoretical priority position and guidance function. The PCR model is not well capable of integrating and screening system information, and its fitting and predicting precisions are not so ideal. The PLS is well capable of integrating and screening system information, and its fitting and predicting precisions are relatively high; it is currently a very effective statistical method for processing collinear data with a small sample and multiple indexes in the sports training area, and it provides a possibility for the empirical test of theoretical models of sports training science, but it has some difficulty to specifically identify the physical meaning of components abstracted. |
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