A study over with four-parameter Logistic and Gompertz growth models

In this study, in addition to classical Logistic and Gompertz models with three parameters, their growth models with four parameters were given. After that it is searched the effect of these growth models on the choice of appropriate growth model by using two separate data sets. For this purpose, classical Logistic and Gompertz growth models and their growth models with four parameters are compared with some model selection criteria such as such as error sum of squares, coefficient of determination, adjusted coefficient of determination and akaike information criteria. For the data set used in this study, it is found that the results of these growth models with four parameters are better than the results of these growth models. Thus, it is considered that these growth models with four parameters can be used in addition to these classical growth models. In addition, some other growth models with four parameters can be investigated for getting the best model choice. Even Logistic and Gompertz models with five and more parameters can be investigated for getting the best model choice.

Automated summary

This study examines the impact of growth models with four parameters on the choice of appropriate growth models, finding that models with four parameters have better results and can be used in addition to traditional growth models. Furthermore, exploring other growth models with four parameters may lead to better model choices.