Multifactor design of experiments software

Multifactor Design of Experiments Software

The term Design of Experiments (DOE) derives from early statistical work performed by Sir Ronald Fisher. [1]

Before Fisher's multi-factor DOE breakthrough, the common experimentation method was conducted using OFAT (one-factor-at-a-time) experimentation. It is a sea-going gentleman named James Lind [2] who today is often attributed as a one-factor-at-a-time experimenter who discovered a cure for scurvy in 1747.

One-factor-at-a-time experimentation reached its zenith with the work of Thomas Edison’s “trial and error” methods.[[3]] OFAT was and remained the basis of scientific experimental design until agricultural needs to furnish growing city populations with food due to concurrent diminishing farm living.

Agricultural science [[4]] advancements served to meet the combination of larger city populations and fewer farms. But for crop scientists to meet widely differing geographical growing climates and needs, it became important to differentiate local growing conditions. For local crops to be used as a guide to feeding entire populations, it became more essential to economically extend crop sample testing to overall populations. As statistical methods advanced (primarily the efficacy of designed experiments instead of one-factor-at-a-time experimentation), representative multi-factor design of experiments began ensuring that inferences and conclusions could profitably extend experimental sampling to the population as a whole. However, a major problem existed in determining the extent to which a crop sample chosen was truly representative. Multifactor DOE began revealing methods to estimate and correct for any random trending within the sample and also in the data collection procedures [5].

Multi factor experimental design software drastically simplifies previously laborious hand calculations needed before the use of computers. Design of experiments results, when discovered accurately with DOE software, strengthens the capability to discern truths about sample populations being tested [6]. Statisticians describe stronger multifactor DOE methods as being more “robust.” (See experimental design.)[[7]]

As design of experiments software advancements gave rise to solving complex multifactor statistical equations, statisticians began in earnest to design experiments with more than one factor being tested at a time. Simply stated, computerized multi-factor design of experiments began supplanting one-factor-at-a-time experiments. Computer software designed specifically for designed experiments became a commercial reality in the 1980s -- available from various leading software companies such as Stat-Ease, [8], JMP [9] and Minitab [10].

Today, multi factor DOE software is a notable tool that engineers, scientists, geneticists, biologists, and virtually all other experimenters and creators, ranging from agriculturists to zoologists, rely upon. DOE software is most applicable to controlled, multi-factor experiments in which the experimenter is interested in the effect of some process or intervention on objects such as crops, jet engines, demographics, marketing techniques, materials, adhesives, and so on. Multifactor DOE software is therefore a valuable tool with broad applications for all natural, engineering, and social sciences.

SOURCES: [11] [12] [13] http://www.wiley.com/WileyCDA/WileyTitle/productCd-EHEP000137.html] [14] [15] [16] [17] [18] [19] [20] [21]