Albin Thomas J, Vink Peter
Delft University of Technology, Faculty of Industrial Design Engineering, United States.
Delft University of Technology, Faculty of Industrial Design Engineering, United States.
Appl Ergon. 2014 Nov;45(6):1392-8. doi: 10.1016/j.apergo.2014.03.006. Epub 2014 Apr 14.
Designers and ergonomists may occasionally be limited to using tables of percentiles of anthropometric data to model users. Design models that add or subtract percentiles produce unreliable estimates of the proportion of users accommodated, in part because they assume a perfect correlation between variables. Percentile data do not allow the use of more reliable modeling methods such as Principle Component Analysis. A better method is needed.
A new method for modeling with limited data is described. It uses measures of central tendency (median or mean) of the range of possible correlation values to estimate the combined variance is shown to reduce error compared to combining percentiles. Second, use of the Chebyshev inequality allows the designer to more reliably estimate the percent accommodation when the distributions of the underlying anthropometric data are unknown than does combining percentiles.
This paper describes a modeling method that is more accurate than combining percentiles when only limited data are available.
设计师和人体工程学家有时可能限于使用人体测量数据的百分位数表来对用户进行建模。增加或减去百分位数的设计模型会产生对适用用户比例的不可靠估计，部分原因是它们假定变量之间存在完美的相关性。百分位数数据不允许使用更可靠的建模方法，如主成分分析。因此需要一种更好的方法。
描述了一种使用有限数据进行建模的新方法。它使用可能相关值范围的集中趋势度量（中位数或均值）来估计组合方差，结果表明与组合百分位数相比，这种方法能减少误差。其次，当基础人体测量数据的分布未知时，使用切比雪夫不等式使设计师能够比组合百分位数更可靠地估计适用百分比。
本文描述了一种在仅有有限数据时比组合百分位数更准确的建模方法。
