Browsing by Author "Yeşilova, A."

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Analysis of Pulmonary Function Test Results by Using Gaussian Mixture Regression Model
(National Scientific Medical Center, 2021) Abut, S.; Doğanay, F.; Yeşilova, A.; Buğa, S.
Background: FEV1/FVC value is used in the diagnosis of obstructive and restrictive diseases of the lung. It is a parameter reported in the literature that it varies according to lung disease as well as weight, age and gender characteristics. Objective: The aim of this study is to investigate the relationship between age, weight, gender and height characteristics and FEV1/ FVC value using a heterogeneous population using Gaussian mixture regression method. Material and methods: GMR was used to separate the data into components and to make a parameter estimation for each component. The analysis performed on this model revealed that the patients were divided into 5 optimal groups and that these groups showed a regular transition from obstructive pattern to restrictive pattern. Results: The mean values of the components for FEV1/FVC were found as 50.071 (3.238), 67.034 (1.725), 82.156 (1.329), 93.592 (1.041), 98.466 (0.303), respectively. The effect of the weight on the components in terms of parameter estimation and standard errors of the components was determined as 0.445 (0.129)**, 0.226 (0.053)**, 0.173 (0.053)**,-0.036 (0.026),-0.040 (0.018)*, respectively. Conclusion: Direct proportional relationship between the patient's weight and the severity of the obstructive pattern, and between the severity of the disease and the age of the patient in both the obstructive and restrictive pattern are explicitly proved. Furthermore, it has been revealed that data sets containing heterogeneity can be analysed by dividing them into sub-components using the GMR model. © 2021, National Scientific Medical Center. All rights reserved.
The Application of Overdispersion and Generalized Estimating Equations in Repeated Categorical Data Related To the Sexual Behaviour Traits of Farm Animals
(Asian Network for Scientific Information, 2007) Yeşilova, A.; Yilmaz, A.
In this study, the Poison regression, negative binomial regression and generalized estimating equations were applied to the repeated measurements based on count data obtained from the sexual behaviors of ram lambs. Negative binomial regression was more effective to handle the over dispersion that causes bias in parameter estimations in Poison regression. The generalized estimating equations were used for analyzing repeated categorical data. GEE estimates were obtained by using the exchangeable working correlation. As a result of GEE analyses, it was concluded that flehmen lip curl response, tail raising, mount duration, vocalization and weight of the ram lamb were statistically important (p<0.05) for mount frequent. However, the anogenital sniff found be not significant. © 2007 Asian Network for Scientific Information.
Effect of Some Trichoderma Species on Germination Rate of Nettle (Urtica Dioica L.)
(Centenary University, 2019) Güneş, H.; Demirer Durak, E.; Yeşilova, A.; Demir, S.
Plants belonging to the Urticaceae family, including 48 genera and 1050 species in the world, are used extensively in many areas from past to present for different purposes. The aim of this study was to determine the parameters that promote germination of U. dioica plant in order to contribute to the studies aimed at increasing the use of nettle in large areas for agricultural and pharmacological purposes. Trichoderma harzianum, T. virens, T. asperellum fungi, Hoagland nutrient solution and Potato Dextrose Agar (PDA), Water Agar and blotter germination medium were used to determine the effects on germination of nettle seeds. The study was conducted in vitro with 4 replications. As a result of the experiment, it was determined that all Trichoderma species promoted germination of nettle plant with nutrient solution in blotter medium and increased by 62.5% compared to control. In this context, it has shown that will be beneficial transferring this effect seen in vitro conditions to in vivo conditions. © 2019, Centenary University. All rights reserved.
Evaluation of Overdispersed Data Set by Using Generalized Linear Mixed Model Approach
(Centenary University, 2016) Ser, G.; Yeşilova, A.
The aim of this study is to investigate the problem of overdispersion frequently observed in the data sets having Poisson distribution, in which the variation is larger than the mean. Data taken Turkish Statistical Institute (TUIK) covered between 2010 and 2015 were consist of kids reared in eighteen city of Turkey. Three different model algorithm are generated in the generalized linear mixed model approach to eliminate the problem of overdispersion in the data set. The study was conducted in two steps. In the first step, we specified the model algorithm showing overdispersion case. In the second step, however, we used the two model algorithm to overcome the elimination of this overdispersion problem. The standard errors estimated in the case of overdispersion were smaller than the case where overdispersion was eliminated. Nevertheless, in case of overdispersion in the data set, it was determined that there were statistically significant differences among factors of years for population size of kid (P<0.0001), whereas these differences among years for population size of kids were not significant when overdispersion were eliminated statistically. Consequently, the present results showed that overdispersion in data set can led to important misunderstandings, if the case of this overdispersion that data sets is ignored. In generalized linear mixed model approach, as an alternative, the use of negative binomial distribution instead of Poisson distribution or adding the random effects under Poisson distribution assumption in model algorithms occurred presents the effective alternative solutions to overcome the overdispersion case. © 2016, Centenary University. All rights reserved.
Gender Mainstreaming Role Preferences and Perceptions of University Students Enrolled at Van School of Health
(Yuzuncu Yil Universitesi Tip Fakultesi, 2021) Karaaslan, S.; Sahin, H.G.; Orhun, R.; Gunbatar, N.; Yeşilova, A.; Erden, C.T.
Investigating university students’ perceptions on the gender roles attributed to men and women in social contexts is highly important for promoting an egalitarian perspective to these gender roles for future generations. This study aimed to investigate the perceptions of undergraduates majoring in midwifery and nursing in school of health on social gender roles. The universe of the cross-sectional study included all the 980 undergraduates enrolled in midwifery and nursing departments in school of health. of these, 648 students including 343 female and 305 male students who consented to participate in the study were included in the study, which accounted for 66.1% of the universe. Data were collected using a survey developed by the researchers. The results indicated that the students’ views regarding social gender equality were not sufficiently egalitarian and male students held more traditional views compared to female students, implicating that university students should not only be equipped with the basic knowledge of their profession but also their sensitivity for social gender equality should be raised. © 2021, Yuzuncu Yil Universitesi Tip Fakultesi. All rights reserved.
Modeling Relative Risk Assesment for Infected Plants by Eriophyoid Mites (Acari, Prostigmata) Using Poisson Log Linear Regression Model
(Centenary University, 2018) Yeşilova, A.; Denizhan, E.; Çobanoğlu, S.
In Poisson regression, the dependent variable of the mite population relative risk assessment can be estimated as based on countable data. Due to disappearing data and numerous uncountable values of the mite population it became difficult to evaluate the risk factor by linear regression methods. The assessment of variable features of mites depending on conditions is very suitable for Poisson regression modeling system. In the this study, the occurrence of rare events such as the occurrence ratio of infected plants was defined by eriophyid mites on two wheat varieties and four different localities. The study was constructed by two way possibility table depending on plant varieties (Triticum aestivum L. and Secale cereale L. (Poaceae) with four locations (Muradiye, Ahlat, Erciş, Doğu Beyazıt and Iğdır). The reference parameters were Triticum aestivum for varieties, and Muradiye for location, respectively. The risk assessment of infected plants for Secale cereale is 1.245 times higher as compared to Triticum aestivum and this difference was found statistically significant (p<0.05). The risk of infected plants for Iğdır location is 1.101 times higher as compared to Muradiye location (p>0.05). In the Poisson log-linear regression, the dependent variable is a risk ratio or a relative risk can be estimated as well as countable data. Thus, Poisson log-linear regression model is a very effective method for analysis of two-way contingency table. Two-way contingency table is created considering the eriophyid mite infection ratio depending on location and varieties, respectively. © 2018, Centenary University. All rights reserved.
Using Poisson and Negative Binom Regression Models on Birds Population İn Dönemeç Delta
(Centenary University, 2018) Durmuş, A.; Yeşilova, A.; Çelik, E.; Kara, R.
The aim of this study to make a statistical estimate to bird populations in Dönemeç Delta using Poisson and negative binom regression models. According to Poisson regression model a deviance statistic greater than one (1) indicates that there is an over-dispersion in the bird’s population. The over-dispersion value in the Poisson regression was much greater than one (156.615). In contrast, the over-dispersion value in negative binom regression was close to one (1.277). Therefore, parameter estimations were interpreted according to negative binom regression. The effects of seasons, habitats and order (ordo) were found to be statistically significant on population density (p<0.05). Summer season when taken as a reference in all seasons only winter season was statistically significant (p<0.05). The population in sandy, farmland and stream edge habitats seen different according to other habitats (meadow, reeds and marshy) (p<0.05). When the Anseriformes ordo is taken as reference only two bird order were insignificant (p>0.05) according to 13 orders. © 2018, Centenary University. All rights reserved.
Using the Poisson and Negative Binomial Regression Modeling of Zooplankton Aquatic Insect Count Data
(Centenary University, 2017) Erdinç, S.; Yeşilova, A.; Ser, G.
The aim of this study was to use for Poisson and negative binomial regressions in the modelling of zooplankton aquatic insect counts. Poisson regression is frequently used to analyze for dependent variable based on count data. In data sets, generally overdispersion is seen. In such cases, applying Poisson regression causes biased parameter estimations and standart errors. When there is overdispersion in data set, it is better to use negative binomial regression model. In negative binomial regression model, parameter estimations are obtained by considering the effect that stems from overdispersion. The overdispersion and zero-inflated parameter levels range was obtained to be quite high. All of the dependent variables were statistically significant on zooplankton aquatic insect counts (p<0.01) in the negative binomial regression. In the case of station of Haraba was taken as the reference level, most zooplankton aquatic insect counts was at the Yolcati station (7.972 times more), while at least zooplankton aquatic insect counts was at the Çarpanak station (99.59% less) (p<0.01). In the case of month of september was taken as the reference level, zooplankton density in August was found to be higher compared to other months (p<0.01). Because of the overdispersion had a significant effect, negative binomial regression was better results than the Poisson regression. © 2017, Centenary University. All rights reserved.
Using Zero-Inflated Generalized Poisson Regression in Modelling of Count Data
(Centenary University, 2017) Soygüder, S.; Yeşilova, A.; Bora, Y.
In this study zero-inflated generalized Poisson regression was applied to the modelling of mite numbers data based on count. The subjects of the zero-inflated generalized Poisson regression are three parameters as mean, overdispersion and zero-inflated dispersion. The overdispersion and zero-inflated dispersion levels range was obtained to be quite high. However, it was found that zero-inflated data and overdispersion had an important effect on mite counts (p < 0.01). It was obtained that 36% (130 observations) of the total numbers of mite had zero values. The effects of all independent variables were found to be statistically significant on mite counts (p < 0.05). The results showed that the differences among regions and varieties regarding the mite counts were statistically significant (p < 0.01). © 2017, Centenary University. All rights reserved.