The two drugs appear to have similar efficacy in CHB patients. However, 7% of patients on ETV therapy had virological breakthrough, while none of the patients on TDF therapy did.

Aim: This study's objective is to examine linearization deviations in various regression models using a multidisciplinary approach. Methods: Curve estimate models' accuracy for each type of data was tested using social, financial, and medical data sets. Results: Although the power of a given model reduces with non-normal distributions, linearization in the social sciences is more efficient and has less variation from regression points in parametric equations. However, single-center distributions in the social sciences typically lead to nonparametric distributions. When compared to social sciences and health sciences, the effectiveness of linearization in financial sciences is higher. The original essence of financial techniques and models is frequently present, and they test their presumptions. Financial models and assumptions are more linear than those seen in real life since they correspond to artificial systems that individuals have developed, making them better suited for predetermined formulas. The rise in linearization issues in the field of finance, which has been increasingly common in recent years, is a symptom of this together with behavioral finance. Studies in the realm of health and well-known models were discovered to have the highest linearization deviations. Exponential or growth functions exhibit the highest linearization deviations in processes like growth, proliferation, and the spread of disease or pandemics. The data display considerable departures from normality and linearization, particularly in animal trials with very small statistical units or research conducted on a particular population. Conclusion: Despite research on R2's explanatory capacity in regression, there aren't enough studies in various fields that concentrate on R2's departures from linearization. Additionally, no study was located in which the subject's mathematical foundation was examined and cross-compared across various data sets. As a result of this feature, the research represents a field first. The research's ability to pragmatically assess the distinctions between disciplines, made possible by its multi-disciplinary nature, is another unique aspect of the work.

The use of credit for various occasions has become a routine in our lives. In return, banking and financial institutions require to determine whether the loan demands from them contain any risk. Accordingly, these institutions have been increased their activities in determining whether credit rating models from past credit records of the person applying for the loan works properly. Machine learning-based technologies have opened a new era in this field. AI and machine learning based methods for credit scoring are currently implemented by banking or non-banking financial institutions. Employed models are to extract meaningful features from the required data in which wide variety of information available. In this study, credit risk assessment is conducted using boosting methods such as CatBoost, XGBoost and Light GBM. To this aim, Kaggle Home Credit Default Risk dataset is used and the effect of crediting tendency on the results is also considered. The results have shown that gradient boosting methods provide results that are close to each other, and crediting tendency produces better AUC score in CatBoost while it causes a small decrement in AUC score of XGBoost and LightGBM.

With the development of computer technologies and invention of internet, many concepts have entered our lives. With the starting of wide usage of globalized internet network, concept of machine learning has emerged in time for smarter management of data flow in big dimensions. In line with technological developments, all activities began to be carried to digital environment and as a result of this, concept of e-commerce has entered our lives. E-commerce is one of the areas where machine learning is used most widely. By examining product purchasing situations in accordance with data available at the enterprises, various researches have been made for selection of most appropriate model in order to predict future data. In the study it was mentioned about concepts of e-commerce and machine learning and by applying Logistic Regression, Naïve Bayes and Support Vector Machines being machine learning classification algorithms, it has been aimed to determine the model having best accuracy ratio.

Amaç –Bu çalışma da, Türkiye’de katılım bankalarının finansal performanslarının hane halkı gelir grupları ile ilişkisi incelenmiştir. Yöntem –Çalışma da TÜİK hane halkı gelir grupları ile İslami Finansal Hizmetler Kurulu (IFSB) tarafından Türkiye için derlenmiş olan varlık getirisi (ROA), likidite/varlık oranı (LR) ve varlık/sermaye oranı (CA) değişkenleri kullanılmıştır. Tüketici Güven İndeksi (TGI) ve Tüketici Fiyat Endeksi (TUFE) verileri ise araştırmanın kontrol verileridir. Araştırmada 2013-2018 yılları arasındaki veriler kullanılmıştır. Bulgular –Araştırma sonuçlarına göre, incelenen zaman dilimi içerisinde tüm gelir gruplarının gelir düzeylerinde bir artış olduğu görülmüştür. Ancak zaman içerisinde, gruplar arasındaki gelir dağılımı farkının da arttığı görülmüştür. Korelasyon analizi sonuçlarına göre CA ile yıl arasında istatistiksel olarak anlamlı ve negatif yönde (p<0.01); TGI ile istatistiksel olarak anlamlı ve pozitif yönde ilişki bulunmuştur (p<0.01). DLR ile yıl arasında istatistiksel olarak anlamlı ve pozitif yönde (p<0.05); TGI ile istatistiksel olarak anlamlı ve negatif yönde ilişki bulunmuştur (p<0.01). ROA ile TUFE arasında istatistiksel olarak anlamlı ve pozitif yönde ilişki bulunmuştur (p<0.05). Logit model sonuçlarına göre; katılım bankalarının varlık/sermaye oranları üzerinde, yılın eksi ve anlamlı bir katkısının olduğu, diğer bir ifadeyle zaman içerisinde varlık/sermaye değerlerinin düştüğü görülmektedir. Ancak hane halkı gelir grubu tek değişkenli analizde anlamlı etkiye sahip olsa da, çok değişkenli analizde etkisi anlamlı değildir (p>0.05). Tartışma –Elde edilen sonuçlar, Türkiye’deki katılım bankalarının, daha düşük gelir gruplarında, daha sağlam ve uzun vadede kalıcı gelir elde edebileceğini göstermektedir.

As statistical inference procedures, both the univariate techniques (t test and ANOVA) and MANOVA are used to assess the statistical significance of differences between groups. The univariate procedures iticinde the t test for two group situations and analysis of variance for situations with three or more groups defined by two or more independent variables.

Bu çalışmada, 1990-1999 ve 2000-2007 olmak üzere iki dönem için illerin sosyoekonomik gelişmişlik endeksinin faktör analizi tekniği ile bulunması ve iki dönem için endeksler arasındaki farklılıkları belirleyerek, ileride öncelikle kalkınma ile ilgili yapılacak çalışmalara özellikle verimliliğin belirlenmesi açısından ışık tutması amaçlanmıştır. Sosyoekonomik gelişmişlik endeksi, temelde Devlet Planlama Teşkilatı tarafından yapılan çalışmalardaki değişkenleri referans alarak belirlenmiştir. Sonuç itibari ile; araştırmada iki dönem arasındaki endeks farkları, illerin yıllar bazında sosyoekonomik bakımdan gelişme gösterip göstermediğini ortaya koymuştur. Bazı illerin endeks değerlerinin pozitif olduğu ancak bir önceki döneme göre düşüş gösterdiği görülmüştür.

The use of machine learning and decision tree methods with respect to modelling of big data has been increasing gradually. The data mining has focused on some methods for obtaining the useful information from big data which has not been recognized. The data mining and statistics concentrates upon the identification of structure. The concept of machine learning which evolved out of data mining as a need consists of models and algorithms which analyzes big data and may make significant deductions from data by benefitting from statistics and software. It provides convenience to modelling the big data that the decision tree methods are nonparametric methods. In this paper, the classification tree was created based on C5.0 algorithm by using R programming language.