Abstract

          Binary logistic regression model used in data classification and it is the strongest most flexible tool in study cases variable response binary when compared to linear regression. In this research, some classic methods were used to estimate parameters binary logistic regression model, included the maximum likelihood method, minimum chi-square method, weighted least squares, with bayes estimation , to choose the best method of estimation by default values to estimate parameters according two different models of general linear regression models ,and different s

Background: Periodontal diseases are inflammatory disorders caused by the accumulation of oral biofilm and the host response to this accumulation which characterized by exaggerated leukocytes and neutrophils attraction to the sites of inflammation by chemoattractants which are a very important part of the pathogenesis of periodontal diseases. This study aimed to determine and compare the clinical periodontal parameters and the leukocyte cell types in the peripheral blood between patients with gingivitis and periodontitis with different severities compared to healthy controls. Materials and methods: This study included 150 male subjects aged between 35-50 years. They were divided into three groups: gingivitis group (n=30), periodontitis p

This experiment was conducted to study the effect of feeding diets containing different levels of parsley on the blood biochemical characteristics of local Iraqi geese. A total of twenty-four local geese, one year old, were used in this experiment during the period from the beginning of October to the end of December. The birds were allocated for Four treatment groups consisted of six geese each. Treatment groups were as follows: Control diet (T1) (free from parsley), T2: Control diet + 80 g / d parsley, T3: Control diet + 160 g / d parsley; T4: Control diet + 240 g / d parsley. At the end of experiment, blood samples were obtained from all geese by venipuncture from brachial vein and blood plasma samples were prepared. Blood biochemical tr

The aim of the research is to study the comparison between (ARIMA) Auto Regressive Integrated Moving Average and(ANNs) Artificial Neural Networks models and to select the best one for prediction the monthly relative humidity values depending upon the standard errors between estimated and observe values . It has been noted that both can be used for estimation and the best on among is (ANNs) as the values (MAE,RMSE, R2) is )0.036816,0.0466,0.91) respectively for the best formula for model (ARIMA) (6,0,2)(6,0,1) whereas the values of estimates relative to model (ANNs) for the best formula (5,5,1) is (0.0109, 0.0139 ,0.991) respectively. so that model (ANNs) is superior than (ARIMA) in a such evaluation.