計算機模型可以描述複雜的物理現象，然而這些模型應用在科學研究時其運算需要很長的時間，而且要有特定的實驗設計才能了解現象的本質。在有缺少一個或數個主效應的情形下，因子設計是不適合的，因為在缺少主效應下時其重複實驗不但不能估計誤差，只是產生重複實驗。雖然已經有學者提出許多可替代的設計，但是大部份設計的計算還是很累贅。本篇論文所提出的一些設計是從旋轉平面的二維因子設計發展而來，這些旋轉因子的設計很容易建構而且保有許多標準因子設計中吸引人的性質：（1）在每個維度的投影是均等空間投影；（2）在迴歸模型中，估計效應是不相關的（即正交的）。這些設計被稱為最大化最小拉丁超方陣，其設計與近期學者建構的最小化內點間距離的準則是同等的。

關鍵字：有效相關（(Effect Correaltion）、拉丁超方陣（Latin Hypercube）、最大化最小距離（Maximin Distance）、最大化最小內點距離（Minimum Interpoint Distance）

Computer models can describe complicated physical phenomena. To use these models for scientific investigation, however, their generally long running times and mostly deterministic nature require a special designed experiment. Standard factorial designs are inadequate; in the absence of one or more main effects, their replication cannot be used to estimate error but instead produces redundancy. A number of alternative designs have been proposed, but many can be burdensome computationally. This paper presents a class of designs developed from the rotation of a two-dimensional factorial design in the plane. These rotated factorial designs are very easy to construct and preserve many of the attractive properties of standard factorial designs: they have equally-spaced projections to univariate dimensions and uncorrelated regression effect estimates (orthogonality) . They also rate comparably to maximin Latin hypercube designs by the minimum interpoint distance criterion used in the latter "s construction.

Key Word : Effect Correlation, Latin Hypercube, Maximin Distance, Minimum Interpoint Distance