We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Microsoft® Excel'de Monte Carlo Benzetimi: Gıda Bilimlerinde Kullanılan Doğrusal Olmayan Regresyon İçin Model Parametrelerinin Güven Aralıklarının Belirlenmesiţ.
- Authors
Buzrul, Sencer
- Abstract
Mathematical models have been frequently used in food sciences to describe experimental data. Linear regression is used if the parameters of a model are linear; however, if a model is not linear with respect to its parameters non-linear regression should be used. Linear regression is easier to apply and interpret than the non-linear regression. Model parameters, standard errors and confidence intervals of these parameters, and goodness-of-fit indices can be obtained by applying linear regression in Excel. On the other hand, it is also possible to perform non-linear regression in Excel. However, only model parameters and goodness-of-fit indices can be calculated in this case. That is, uncertainties (standard errors or confidence intervals) of model parameters cannot be obtained. However, parameter uncertainties are one of the important indicators whether a model is used or not for the data being handled, and shareware that perform non-linear regression can be used to obtain them. Another alternative is to apply Monte Carlo (MC) simulation in Excel, which is installed on many personal computers. In this study, application of MC simulation in Excel was explained step by step in details. It was observed that the values of the parameter uncertainties obtained by performing MC simulation in Excel were very close to those obtained by using any shareware. It will be an ordinary and simple process to perform MC simulation (random data generation, use of the Solver tool, etc.) in Excel as the users become familiar with the use of Excel, and this information would be beneficial for many researchers working in different fields of food science (microbiology, biotechnology, unit operations, etc.).
- Subjects
NONLINEAR regression; MONTE Carlo method; MATHEMATICAL models; FOOD science; CONFIDENCE intervals
- Publication
Academic Food Journal / Akademik GIDA, 2021, Vol 19, Issue 3, p291
- ISSN
1304-7582
- Publication type
Article
- DOI
10.24323/akademik-gida.1011223