{"title":"关于一个新的增长模型,即Korkmaz模型与一些增长模型的比较","authors":"M. Korkmaz","doi":"10.7212/ZKUFBD.V8I1.1190","DOIUrl":null,"url":null,"abstract":"For growth models, in addition to some classical growth models, I derived a new model. In this study, I derived a new model by using this expression: “Growth models has generally sigmoidal shape. In this shape there is one inflection point. Until this inflection point the graph is convex that’s until this inflection point the growth rate is increasing. At this infection point the growth rate reaches maximum value. After this inflection point the graph is concave that’s after this inflection point the growth rate is decreasing.” Growth models were generally derived by using the last part of this situation. That’s Growth models were generally derived by using this expression: “Growth rate goes to zero when the time is too large or approaches infinity”. After introducing this new model, namely Korkmaz model, I applied two sets of data. In addition to Korkmaz model, I used growth models such as Logistic, Brody, Gompertz, and Von Bertalanffy. They are compared by using error sum of squares criteria. According to this criteria, it was seen that none of the models used has minimum error sum of squares for each data set. That’s while one model is the best model for one data set, that model could not be the best model for the other data set. Actually, Although Korkmaz model is not the best model for two sets of data by using error sum of squares criteria, Korkmaz model is one of the best models in this study. For that reason, use of Korkmaz model in addition to classical growth models in their studies on growth data was suggested to the researchers using growth models in their studies.","PeriodicalId":17742,"journal":{"name":"Karaelmas Science and Engineering Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On a new growth model namely Korkmaz model compared with Some Growth Models\",\"authors\":\"M. Korkmaz\",\"doi\":\"10.7212/ZKUFBD.V8I1.1190\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For growth models, in addition to some classical growth models, I derived a new model. In this study, I derived a new model by using this expression: “Growth models has generally sigmoidal shape. In this shape there is one inflection point. Until this inflection point the graph is convex that’s until this inflection point the growth rate is increasing. At this infection point the growth rate reaches maximum value. After this inflection point the graph is concave that’s after this inflection point the growth rate is decreasing.” Growth models were generally derived by using the last part of this situation. That’s Growth models were generally derived by using this expression: “Growth rate goes to zero when the time is too large or approaches infinity”. After introducing this new model, namely Korkmaz model, I applied two sets of data. In addition to Korkmaz model, I used growth models such as Logistic, Brody, Gompertz, and Von Bertalanffy. They are compared by using error sum of squares criteria. According to this criteria, it was seen that none of the models used has minimum error sum of squares for each data set. That’s while one model is the best model for one data set, that model could not be the best model for the other data set. Actually, Although Korkmaz model is not the best model for two sets of data by using error sum of squares criteria, Korkmaz model is one of the best models in this study. For that reason, use of Korkmaz model in addition to classical growth models in their studies on growth data was suggested to the researchers using growth models in their studies.\",\"PeriodicalId\":17742,\"journal\":{\"name\":\"Karaelmas Science and Engineering Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Karaelmas Science and Engineering Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7212/ZKUFBD.V8I1.1190\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Karaelmas Science and Engineering Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7212/ZKUFBD.V8I1.1190","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On a new growth model namely Korkmaz model compared with Some Growth Models
For growth models, in addition to some classical growth models, I derived a new model. In this study, I derived a new model by using this expression: “Growth models has generally sigmoidal shape. In this shape there is one inflection point. Until this inflection point the graph is convex that’s until this inflection point the growth rate is increasing. At this infection point the growth rate reaches maximum value. After this inflection point the graph is concave that’s after this inflection point the growth rate is decreasing.” Growth models were generally derived by using the last part of this situation. That’s Growth models were generally derived by using this expression: “Growth rate goes to zero when the time is too large or approaches infinity”. After introducing this new model, namely Korkmaz model, I applied two sets of data. In addition to Korkmaz model, I used growth models such as Logistic, Brody, Gompertz, and Von Bertalanffy. They are compared by using error sum of squares criteria. According to this criteria, it was seen that none of the models used has minimum error sum of squares for each data set. That’s while one model is the best model for one data set, that model could not be the best model for the other data set. Actually, Although Korkmaz model is not the best model for two sets of data by using error sum of squares criteria, Korkmaz model is one of the best models in this study. For that reason, use of Korkmaz model in addition to classical growth models in their studies on growth data was suggested to the researchers using growth models in their studies.