Filiz Ersoz
Dr. Filiz Ersöz started her career in 1989 as a “Statistician” at the Turkish Statistical Institute. Then, she worked as NATO NAAG Force Coordinator and Defense Industry Cooperation Coordinator as an "Operational Research Expert" at the Land Forces Command between 2001-2006, as “Probability and Stochastic Processes Expert” at the Turkish Military Academy between 2006-2012, and an academician in the Department of Industrial Engineering at Karabük University between 2012-2023. She is the author of “Statistics-I”, "Statistics-II“, "Data Mining Techniques and Applications”, “Statistical Data Analysis with IBM SPSS” and “Simulation and Modeling”. She has published more than 100 articles in the ISI, Scopus, IEEE etc. Her courses include "Probability and Statistics", "Engineering Statistics", "Research Methods", "Multivariate Statistical Analysis", "Data Mining", "Simulation and Modeling", "Statistical Quality Control”, “Computer Applied Statistics" and "Decision Analysis".
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Papers by Filiz Ersoz
The results revealed that the chicks hatched in May had nearest values to the combined material. The results showed that Richards growth model was the best model in explaining the growth of the chicks for the three hatching periods and also for the combined material.
The models of Brody, Bertalanffy, Stevens, Gompertz and Logistic followed Richards model in the descending order. The fitting criteria were a value, error sum of squares (HKT) and determination coefficient. In the Richards model the values for α, HKT an r2 were 3141.12, 63746.65 and 0.9972, respectively. The error sum of squares for the other models were 65494.7, 65496.6, 127281.9, 192664.8, 423222.9, in the above order.
annually by the Heritage Foundation under the “Economic Freedom Index”. The concept of
freedom is an important ideal for humanity. In terms of countries, economic freedom can be
regarded as a sign of sustainable growth and prosperity. Countries included in the index are
scored and ranked by twelve independent variables that determine the economic freedom score.
In this study, Clustering models have been used to find relationships between variables that
determine the economic freedom score of Organization for Economic Co-operation and
Development (OECD) member countries according to the 2018 Economic Freedom Index.
OECD member countries have been identified with similar and non-similar countries according
to selected indicators. As a result of the study, the best cluster selection was made by comparing
the different clustering algorithms, and the similarities and differences between the OECD
countries in the literature are presented.
The results revealed that the chicks hatched in May had nearest values to the combined material. The results showed that Richards growth model was the best model in explaining the growth of the chicks for the three hatching periods and also for the combined material.
The models of Brody, Bertalanffy, Stevens, Gompertz and Logistic followed Richards model in the descending order. The fitting criteria were a value, error sum of squares (HKT) and determination coefficient. In the Richards model the values for α, HKT an r2 were 3141.12, 63746.65 and 0.9972, respectively. The error sum of squares for the other models were 65494.7, 65496.6, 127281.9, 192664.8, 423222.9, in the above order.
annually by the Heritage Foundation under the “Economic Freedom Index”. The concept of
freedom is an important ideal for humanity. In terms of countries, economic freedom can be
regarded as a sign of sustainable growth and prosperity. Countries included in the index are
scored and ranked by twelve independent variables that determine the economic freedom score.
In this study, Clustering models have been used to find relationships between variables that
determine the economic freedom score of Organization for Economic Co-operation and
Development (OECD) member countries according to the 2018 Economic Freedom Index.
OECD member countries have been identified with similar and non-similar countries according
to selected indicators. As a result of the study, the best cluster selection was made by comparing
the different clustering algorithms, and the similarities and differences between the OECD
countries in the literature are presented.