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Charts for Simplified Bivariate Anthropometric Design

机译:简化二元人体测量设计的图表

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Generalized workspace and clothing design problems often arise in which two anthropometric constraints must be considered simultaneously in order to accommodate some specific target percentage of the population. In the theoretical (and unlikely) instance in which the two variables are perfectly positively correlated, the problem is readily solved using univariate percentile information. However, in the more realistic case in which the two variables are less than perfectly positively correlated, bivariate percentile charts are required. These bivariate charts are rarely available but may be computer-generated from the univariate data and the correlation between the variables after assuming some appropriate bivariate distribution (usually Gaussian). However, such computer access is not always available especially when a quick estimate is needed. This paper presents a simplified approach to bivariate design based on the workable assumption that the bivariate target percentage will be met by using the same (to be determined) univariate cutoff value for each variate. This cutoff value depends on the target percentage value and on the degree of correlation between the variables and the assumption that the data are adequately represented by a Gaussian bivariate distribution. The method takes advantage of simple charts prepared expressly for this purpose and several of which are presented herein. The method also has utility in a number of practical and common problems as well as being suitable for student use.
机译:广义的工作区和服装的设计问题经常出现,其中,两个人体测量约束必须以适应人口的一些具体的目标百分比同时考虑。在理论(和不太可能)实例,其中,两个变量是完全正相关,该问题中使用单变量百分信息迎刃而解。然而,在更真实的情况,其中,两个变量是小于完全正相关,需要二元百分图表。这些二元图表很少可用的,也可以是计算机产生的,从单变量数据,并假设一些适当的二元分布(通常是高斯)之后的变量之间的相关性。然而,这样的电脑上网并不总是可用的需要快速估计时尤其如此。本文提出了一种简化的方法来设计二元基于所述可行的假设,即目标二元百分比将通过使用相同的(待定)单变量截止每个变量值来满足。该截止值取决于目标百分比值和所述变量和数据被适当地由高斯二元分布表示的假设之间的相关程度。该方法利用简单的图表清楚地制备用于该目的和几个其在此呈现。该方法还具有一些实际和常见的问题效用以及作为适合于学生使用。

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