Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage and Selection Operator (Lasso), and Tikhonov Regularization (Ridge). The simulation studiesshow that the performance of our method is better than the othersaccording to the error and the time complexity. Thesemethodsare applied to a real dataset, which is called Rock StrengthDataset.The new approach implemented using the Gibbs sampler is more powerful and effective than other approaches.All the statistical computations conducted for this paper are done using R version 4.0.3 on a single processor computer.
The tests that measure special strength defined by speed contributes a great deal in evaluating the players' weaknesses and strengths so as to aid coaches judge their players according to scientific and objective measurements. The problem of the study lies in answering the following question : is there a test that measures legs' vertical strength defined by speed especially for youth basketball players? The aim of the research was to construct and standardize a test for measuring legs' vertical strength defined by speed in youth basketball. The subjects of the study were 74 youth basketball players from Baghdad. The researchers concluded that the test measures leg's vertical strength defined by speed for youth basketball players as well as
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Binary polymer blend was prepared by mechanical mixing method of unsaturated polyester resin with Nitrile Butadiene Rubber (NBR) with different weight ratios (0, 5, 10 and 15) % of (NBR). Tensile characteristics and wear rates of these blends were studied for all mixing ratios. The microstructure of fracture surfaces of the prepared samples were investigated by optical microscope. The results were showed that strain rates of the resin material increase after blending it with rubber while the ultimate tensile strength and Young’s modulus values of it will decrease. It is also noticed that the wear rate of resin decreases with increasing of (NBR) content.
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... Show MoreBack ground: Glass ionomer materials lack resistance to wear and pressure and are susceptible to moisture during the initial stages of setting and dehydration. So this study was done to assess diametral tensile strength and microhardness of glass ionomer reinforced by different amounts of hydroxyapatite. Materials and methods: In this study a hydroxyapatite material was added to glass monomer cement at different ratios: 10%, 15%, 20%, 25% and 30% (by weight). The diametral tensile strength test described by the British standard specification for zinc polycarboxylate cement was used in this study and the microhardness test was performed using Vickers microhardness testing machine and the microhardness values were calculated and statistical c
... Show MoreThe study of biomechanical indicators in the arc of the run and the upgrading stage is one of the important variables that affect the nature of the upgrading and thus affect the result of the race due to the importance of these stages and the consequent variables during the last steps. That’s why, the jump-trainings based on assistant means or body weight positively affect the step-time for each of the three steps in the acceleration arc. As well as, it focuses on the momentary strength of each step at this stage. It also significantly affects the speed of motor performance to suit the activity in which the runner needs to perform perfect steps with high flow in order to convert the horizontal speed to a vertical one. This is achieved thr
... Show MoreThe estimation of the parameters of linear regression is based on the usual Least Square method, as this method is based on the estimation of several basic assumptions. Therefore, the accuracy of estimating the parameters of the model depends on the validity of these hypotheses. The most successful technique was the robust estimation method which is minimizing maximum likelihood estimator (MM-estimator) that proved its efficiency in this purpose. However, the use of the model becomes unrealistic and one of these assumptions is the uniformity of the variance and the normal distribution of the error. These assumptions are not achievable in the case of studying a specific problem that may include complex data of more than one model. To
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