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Factorial Design:
The factorial experiment design allows the researcher to test two or more variables at the same time. It determines whether the variables interact to produce an observed response or whether the variables operate independently to produce the response. The factorial experiment is used when there are three or more experimental variables. It is applied when the design is completely randomized or is a randomized block or Latin square. The main feature of this design is that the impact of various variables can be examined. It involves some complex computations. We can calculate the interaction of the variables on each other which requires variance analysis and notching out the main effects of each of the variables. Standard deviation and mean squares are calculated to fin doubt the impact of variables. Thus this design determines not only the main effects of each of the variable but also measures the interaction effects of the variables. For example the use of coffee may be preferred with a high content of sugar and high flavour. To test these contents various doses of sugar may be introduced and the number of consumers purchasing them may be recorded to find out their consequences.
Simple or single-stage decision making models are characterized by: a. Event nodes. b. Decision nodes. c. Decision arrows. d. Conditional payoff tables or matrices.
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