Statistical designs for fitting response surfaces incorporating neighbour effects

DOI: 10.18805/BKAP170    | Article Id: BKAP170 | Page : 139-141
Citation :- Statistical designs for fitting response surfaces incorporatingneighbour effects.Bhartiya Krishi Anusandhan Patrika.2019.(34):139-141
Jitendra Kumar, Seema Jaggi, Eldho Varghese, Arpan Bhowmik and Cini Varghese arpan.stat@gmail.com
Address : ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110 012, India.
Submitted Date : 4-03-2019
Accepted Date : 25-04-2019

Abstract

Response Surface Methodology (RSM) approximates the relationship between one or more response variables and a set of experimental variables or factors. In RSM, it is generally assumed that the observations are independent and there is no effect of neighbouring units. But under the situation when the units are placed linearly with no gaps there is high possibility of overlapping or neighbour effects from the adjacent units. So including these effects into the model is of great importance in deciding the precision of the experiment. Further, availability of resources and size of the experiment is important factor in conducting an experiment. As the size increases, cost involved in conducting the experiment increases, thereby decreasing the precision of the experiment. In this study, response surface designs incorporating neighbour effects have been considered. Method of constructing First Order Rotatable Designs with Differential Neighbour Effects (FORDDNE) has been developed in smaller number of runs.

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References

  1. Draper, N.R. and Guttman, I. (1980). Incorporating overlap effects from neighbouring units into response surface models. Applied Statistics, 29(2): 128-134.
  2. Jaggi, Seema, Sarika, Sharma, V.K., (2010). Response surface analysis incorporating neighbour effects from adjacent units. Indian Journal of Agri. Sciences. 80: 719-723.
  3. Khuri, A.I, Cornell, J.A., (1996). Response Surfaces-    Designs and Analysis. Marcel Dekker, New York.
  4. Montgomery, D. C., Peck, E. A., (2006). Introduction to Linear Regression Analysis. John Wiley & Sons, New York.
  5. Sarika, Jaggi, S. and Sharma, V.K. (2013). First order rotatable designs incorporating neighbour effects. Ars Combinatoria. 112: 145-159. 

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