OUTLINEABSTRACTINTRODUCTIONCRITICAL ANALYSIS OF CURRENT PRACTICELITERATURE REVIEWBACKGROUND TO DESIGN OF EXPERIMENTSCASE STUDY - SPROUT SPACECONCLUSIONREFERENCESABSTRACTINTRODUCTIONCRITICAL ANALYSIS OF CURRENT PRACTICELITERATURE REVIEWBACKGROUND TO DESIGN OF EXPERIMENTSAccording to the US National Institute of Science and Technology (NIST) the design of experiments is a systematic, rigorous approach to engineering problem-solving that uses statistical methods to derive valid engineering conclusions all under the constraint of minimal expenditure of engineering resources (NIST, 2012). There are four general problem areas in which DoE is applied: comparative assessment of experimental outputs, screening for important factors, modeling solutions and optimization of the problem space. Although defined as an engineering method, DoE is widely applied to optimization problems in several fields including building performance analysis.As an optimization method DoE uses statistical methods to discover the optimum value from a large problem space of values by methodically sampling the space and then interpolating between sampled values to obtain estimates of non-sampled values. This estimated problem space can then be optimized, to a high degree of accuracy, much faster than executing the full problem space. The concept is best illustrated by a simplified example. Suppose an experiment wishes to optimize an outcome O. Suppose that there are two factors affecting this outcome A and B. A has two possible values A1 and A2 while B has a range from 8 to 14. The experimental table and results are shown in Table 1.Experiment results RunOrder A B Outcome (O) 2 - - 3 3 + - 5 1 - + 4 4 + + 9 \(A_1\)CASE STUDY - SPROUT SPACECONCLUSIONREFERENCES
