Developing A New Dimension of an Applied Exponential Model: Application in Biological Sciences
Modeling of exponential growth or decay is a nonlinear regression technique. In the real world, the exponential growth is often used to model population growth while the exponential decay is often used to a model declining population or a decreasing size. In this study, we try to improve the performance of exponential growth by adding bootstrap and fuzzy techniques. This gives us the option to perform analysis even when there is not enough data. The aim of the current work is to develop a new dimension of an applied exponential analysis with improved results. The suggested method was tested and applied to biological data. The gathered data was compared by measuring the average width of the predicted interval using least squares method and fuzzy method. The result shows that the average width of the predicted interval using least squares method was 0.522 while using fuzzy method was 0.082. This indicated the superiority of the fuzzy regression methodology. Besides that, this paper provides the algorithm for the prediction of cell growth and inferences.
Keywords:bootstrap, exponential growth, fuzzy regression, exponent decay, nonlinear regression
R. J. Tallarida, R. B. Murray, Manual of Pharmacologic Calculations with Computer Programs, Springer, 1981 DOI: https://doi.org/10.1007/978-1-4684-0101-1
A. A Bartlett, “The exponential function-Part I”, The Physics Teacher Vol. 14, No. 7, pp. 393-401, 1976 DOI: https://doi.org/10.1119/1.2339436
W. M. A. W. Ahmad, A. Nor Azlida, D.Yosza, A. H. Nurfadhlina, H. Ruhaya, A. Zalila., B. Adam, Z. Syerrina, “Applied exponential growth regression modeling using SAS: An Alternative Method of Biostatistics”, International Journal of Applied Engineering Research, Vol. 12, No. 18, pp. 7853-7856, 2017
T. H. D. Ngo, C. A. La Puente, C. A. “The Steps to Follow in a Multiple Regression Analysis”, SAS Global Forum 2012: Statistics and Data Analysis, Orlando, USA, Paper 333-2012, April 22-25, 2012
H. Ghosh, S. Wadhwa, Application of fuzzy regression methodology in agriculture using SAS, Indian Agricultural Statistics Research Institute (IASRI), 2001
Ν. Yusop, P. Battersby, A. Alraies, A. J. Sloan, R. Moseley, R. J. Waddington, “Isolation and characterization of mesenchymal stem cells from rat bone marrow and the endosteal niche: a comparative study”, Stem Cells International, Vol. 2018, Article ID 6869128, 2018 DOI: https://doi.org/10.1155/2018/6869128
R. Korner, W. Nather, “Linear regression with random fuzzy variables: extended classical estimates, best linear estimates, least squares estimates”, Information Sciences,Vol. 109, No. 1-4, pp. 95-118, 1998 DOI: https://doi.org/10.1016/S0020-0255(98)00010-3
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