This paper focus on comparing the differences and similarities between the results obtained from Greedy and classical algorithms for integer linear programming (ILP) problems. For this purpose, the solution of the problems related to different models with the purpose function and constraints has been provided by developing a software (Java Program) which solves the Knapsack problems (KP) with Greedy algorithm. Both the classical algorithm and the results obtained from Greedy algorithm are compared for the problems considered here. In this context, the results obtained from algorithms are found to be the same for small-sized pure and 0-1 binary Knapsack problems. Since packet programs are limited in dimension and number of constraints, it becomes difficult to obtain appropriate results from classical algorithms as the dimension of the problem grows. However, Greedy algorithm gives the appropriate results regardless of the dimension and the number of constraints.

Smoking is an important public health problem. Many factors such as age, gender, education, income level and stress affect daily cigarette consumption. Identifying these causes is very important in combating smoking addiction. This study was planned to determine the factors affecting the smoking habits of individuals. The questionnaire was applied to 304 participants who accepted to participate in the study between October and December 2019. Descriptive statistics such as median, mean and frequency were calculated from the data. According to this; 62.8% of the participants are women and 37.2% are men. Average age is 33.18. 62.8% of the participants stated that they do not smoke. Most of the participants who smoke, according to the frequency table, smoke 10 cigarettes a day. Spearman correlation test was used to compare the fit between variables. Negative Binomial regression analyzes were performed to determine the factors affecting smoking habits. Regression models results; it shows that age, education, income, number of children, presence of a smoker in the family, presence of smoker in friends, smoking history variables are effective on individuals' smoking

Bu çalışmada, Türkiye'deki illerin 2004-2018 yılları arasındaki ulaştırma göstergeleri veri setini oluşturan 40 adet değişken kullanılarak kümeleme analizi yöntemleri uygulanmıştır. Analizde kullanılan veri setinin uygunluğunu sağlamak üzere Öklid uzaklık ve Min-Max standartlaştırma ölçüleri kullanılmıştır. Çalışmada, ulaştırma göstergelerini oluşturan 40 adet değişken dikkate alınarak Türkiye'deki mevcut 81 İlin kümelenme yapısının ortaya konulması amaçlanmıştır. Tek-bağlantı yöntemi, kullanılan hiyerarşik yöntemler arasında kofenetik korelasyon katsayısı değerinin yüksekliği sebebiyle en başarılı yöntem olarak kabul edilerek sonuçları değerlendirilmiştir. Ayrıca hiyerarşik olmayan yöntemlerden olan kortalamalar yöntemi sonuçlarının incelenmesi ve yorumlaması yer almaktadır. Küme sayısı, küme geçerlilik indeksleri yardımı ile beş olarak belirlenmiştir. Elde edilen sonuçlar değerlendirildiğinde, ulaştırma göstergeleri bakımından büyük kent merkezlerine sahip illerde diğer illere göre daha belirgin farkların ortaya çıktığı görülmektedir.

Classical regression assumptions are not valid in count models. Therefore, Poisson and negative binom distribution are the most common methods for count data. The Poisson model can be used in case of equal spread, while negative binom distributions in case of overdispersion. In practice, data is often over dispersed. If there are too many zero values in the count data, zero-inflated Poisson models in case of equal spread, zero-inflated negative binom models, Poisson Hurdle and negative binom Hurdle models or their generalized models can be preferred in case of overdispersion. These models generally focus on modeling the conditional average of the dependent variable. However, conditional average regression models may be sensitive to outliers of the dependent variable or provide no information about other conditional distribution properties. In this case, quantile regression, which is one of the robust methods for count data, can be used. The quantile regression has the advantages of robust prediction in the presence of outliers. In this study, count data was taken to show the dependent variable number of articles. Independent variables include of gender, marital status, number of children under the age of 5, prestige of the doctorate, and the number of articles by the consultant in the last 3 years. After applying Poisson and negative binom distribution in the study, 25%, 50%, 75% and 90% quantile regression estimates were obtained.

Increasing the success levels of university students and ensuring that they are trained as qualified individuals are a driving factor for the countried economic and social developments. In the latest literature review, it is reported that many academic studies for this purpose are performed by factor analysis but no studies have been done to determine the factors affecting the success of the students studying in the Department of Econometrics. In this study, Manisa Celal Bayar University Faculty of Economics and Administrative Sciences in the period of 2015-2016 academic year, a questionnaire consisting of 54 questions was given to 210 university students from Department of Econometrics. As a result of the factor analysis, 8 factors were found to affect students achievements in this study. These factors explain 65% of the variance. Keywords:Multivariate Statistical Analysis, Factor Analysis, Student Success Ekonometri Bölümü Öğrencilerinin Başarılarını Etkileyen Faktörlerin