This study is dedicated to solving multicollinearity problem for the general linear model by using Ridge regression method. The basic formulation of this method and suggested forms for Ridge parameter is applied to the Gross Domestic Product data in Iraq. This data has normal distribution. The best linear regression model is obtained after solving multicollinearity problem with the suggesting of 10 k value.
The problem of Bi-level programming is to reduce or maximize the function of the target by having another target function within the constraints. This problem has received a great deal of attention in the programming community due to the proliferation of applications and the use of evolutionary algorithms in addressing this kind of problem. Two non-linear bi-level programming methods are used in this paper. The goal is to achieve the optimal solution through the simulation method using the Monte Carlo method using different small and large sample sizes. The research reached the Branch Bound algorithm was preferred in solving the problem of non-linear two-level programming this is because the results were better.
Abstract: Residual ridge resorption is chronic, progressive, irreversible and cumulative condition associated with teeth loss. Management of a residual ridge with severe resorption to fulfill the patient’s esthetic and functional requirements is quite difficult for the practitioner and also in the construction of an acceptable appliance. As the height of the edentulous ridge reduces the mandibular denture will be improperly function. Severe resorption of the mandibular alveolar ridge may cause instability and discomfort of the convention acrylic resin denture. Dealing with this condition requires clinical skills and knowledge. Treating the severely resorbed mandibular ridge is a challenging effort for prosthodontics. This research present
... Show MoreThe last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of
... Show MoreSewer sediment deposition is an important aspect as it relates to several operational and environmental problems. It concerns municipalities as it affects the sewer system and contributes to sewer failure which has a catastrophic effect if happened in trunks or interceptors. Sewer rehabilitation is a costly process and complex in terms of choosing the method of rehabilitation and individual sewers to be rehabilitated. For such a complex process, inspection techniques assist in the decision-making process; though, it may add to the total expenditure of the project as it requires special tools and trained personnel. For developing countries, Inspection could prohibit the rehabilitation proceeds. In this study, the researchers propos
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The logistic regression model is one of the nonlinear models that aims at obtaining highly efficient capabilities, It also the researcher an idea of the effect of the explanatory variable on the binary response variable. &nb
... Show MoreThis research aims to demonstrate the knowledge pillars of the product life cycle assessment technique and how to measure the cost according to this technique, and to clarify its role in reducing costs, improving product quality and optimizing the use of available resources, and a set of results has been reached, the most important of which are: The separation of environmental costs through the use of product life cycle assessment technique helps the Management in handling the increase of these costs, reducing the rates of environmental pollution and preserving resources, which contributes to achieving the sustainability of the product, and based on the results obtained, a set of recommendations were presented, the most important of which w
... Show MoreThe efficiency of management is determining factor for the success or failure of agricultural projects generally and Livestock particularly achieving its objectives. Therefore, this research came to diagnose the most important variables that determine the efficiency of management using the probability regression models to measure the probability of management efficient of broilers production projects using random sample included (60) broilers projects represented 11.6% of Baghdad province (research community) in 2016. After estimating the relationship between the management efficiency (descriptive dependent variable) and the independent variables affecting it (age, educational level, production index (PI), experience). The results
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The multiple linear regression model of the important regression models used in the analysis for different fields of science Such as business, economics, medicine and social sciences high in data has undesirable effects on analysis results . The multicollinearity is a major problem in multiple linear regression. In its simplest state, it leads to the departure of the model parameter that is capable of its scientific properties, Also there is an important problem in regression analysis is the presence of high leverage points in the data have undesirable effects on the results of the analysis , In this research , we present some of
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