SUMMARYThe existence of change point in the Turkish earthquake data is investigated. For this purpose, the 218 earthquake data of magnitude 5 and higher, between the north (39.00-42.008) and the east (26.00-45.008) coordinates in Turkey from 12 July 1900 to 28 February 2007 are used. The characteristic function of the magnitude is derived. Poisson distribution is used to describe the recurrence times. In addition, using the compound Poisson process, the expected value and variance are estimated and computed for the loss of life and damaged buildings after the change point.

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Bilimsel araştırmalarda bilinmeyen kitle ortalamasına ilişkin çıkarsamalar yapmak büyük önem taşır. Bilindiği gibi tek etken için ikiden fazla kitle ortalaması arasındaki farkın önem kontrolü Tek yönlü Varyans Analizi (ANOVA) ile araştırılır. İki ya da daha fazla etken ve her bir deneme kombinasyonunda birden fazla tekrarın söz konusu olduğu denemelerde ise çok etkenli deney düzenleri söz konusudur.A etkeninin düzey sayısı a, B etkeninin düzey sayısı b ve herbir deneme kombinasyonundaki tekrar sayısı k olmak üzere iki faktörlü bir ANOVA modeli, eşitliği ile tanımlanır. Anahtar sözcükler: ANOVA, Çok etkenli deney düzenleri, Kareler toplamı Comparisons of Sum of Squares Methods in ANOVA Models SummaryThe total sum of squares in ANOVA models is decomposed and the significances of the main term and interaction terms are tested. The four different methods called as Type I, II, III, and IV are used for calculating the sum of squares of the main effects and interactions. The model and data structures are crucial in the selection of the most appropriate method for applications. However, Type III method is adapted as the default sum of square method in most of the standard statistical software packages regardless of model and data structures. If the appropriate method is not selected, it directly affects the value of the F statistic and it may cause statistically invalid inferences. In this paper, the use of the sum of squares methods for various cases are indicated and the differences between the methods are discussed on two examples defined by a two-way ANOVA design.

Beta regression assumes that the dependent variable follows a beta distribution and that its mean is related to a set of exploratory variables through a linear predictor with unknown coefficients and a link function. The model also includes a dispersion parameter. This paper describes the beta regression model along with its properties. The application of the model is made on well-being index data of Turkey 2015, which comprise the dimensions of housing, work, income and wealth, health, education, environment, safety, civic participation and access to infrastructure services and social life. As the life satisfaction index lies between 0 and 1 and the values close to 1 refers to a better level of life. Beta regression fits the data well and the regression parameters are well interpreted in terms of the mean of the response variable.

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