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IJSTR >> Volume 9 - Issue 1, January 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Approximation Of Randomized Block Design Towards Fuzzy Multiple Linear Regression: A Case Study In Health Sciences

[Full Text]

 

AUTHOR(S)

Wan Muhamad Amir W Ahmad, Soban Q. Khan, Rabiatul Adawiyah Abdul Rohim, Nor Azlida Aleng, Farah Muna Mohamad Ghazali

 

KEYWORDS

Qualitative predictor variables, ANOVA, Design of Experiment, New Dimension, Multiple Linear Regression, Randomized Block Designs and Fuzzy Linear Regression

 

ABSTRACT

ANOVA also provides a method of data analysis that is motivated by consideration of the experimental design or Design of Experiment (DOE). In this paperwork, a new dimension of the methodology by involving fuzzy regression approach to randomized block designs will be introduced, which is involving qualitative predictor variables under consideration on multiple linear regression. The idea from this research will be a useful thread for establishing comprehensive connectivity between randomized block designs and regression. The researchers can conclude that fuzzy MLR can predict much better compared to MLR itself.

 

REFERENCES

[1] Armstrong, R. A., et al. .“An introduction to the analysis of variance (ANOVA) with special reference to data from clinical experiments in optometry.” Ophthalmicand Physiological Optics 20(3): 235-241. 2000.
[2] Shahbaba, B. Analysis of Variance (ANOVA) Biostatistics with R (pp. 221-234; Springer. 2012.
[3] Montgomery, D.C.. Design and Analysis of Experiments, 5th edn. John Wiley, & Sons, Inc, New York, 2009.
[4] Churchill, G. A.. Using ANOVA to analyze microarray data. Biotechniques, 37(2), 173-177, 2004.
[5] Neter, J., Kutner, M.H., Nachtsheim, C.J., and Wasserman, W. Applied Linear Statistical Models. 4th Edition, WCB McGraw-Hill, New York, 1996.
[6] Tanaka H., Uegima S., Asai K.,. Linear regression analysis with the fuzzy model. IEEE Trans. Systems, Man and Cybernetics, 12, 903-907, 1982.
[7] Bárdossy, A.. Note on fuzzy regression. Fuzzy Sets and Systems, 37(1), 65-75, 1990.
[8] Kao, C., & Chyu, C.-L.. Least-squares estimates in fuzzy regression analysis. European Journal of Operational Research, 148(2), 426-435. 2003.
[9] Kumar, A., et al.,.Fuzzy regression interval models for forewarning onion thrips. Computing for Sustainable Global Development (INDIACom), International Conference on, IEEE, 2014.
[10] Slowinski, R. 1998. Fuzzy Sets in Decision Analysis, Operations Research, and Statistics. Kluwer Academic Publisher, USA, 1998.