It is well-known that the existence of outliers in the data will adversely affect the efficiency of estimation and results of the current study. In this paper four methods will be studied to detect outliers for the multiple linear regression model in two cases : first, in real data; and secondly, after adding the outliers to data and the attempt to detect it. The study is conducted for samples with different sizes, and uses three measures for comparing between these methods . These three measures are : the mask, dumping and standard error of the estimate.
Teachers should have acceptable ratios of multiple intelligences. Knowing their most dominate intelligence, teachers will be capable of understanding and considering the multiple intelligences of their students. Accordingly, they will be able to use Gardner’s theory as an effective approach of teaching for all kinds of learners and not limited to a specific type of students especially those who have high ratios of verbal-linguistic and logic-mathematical intelligences
Abstract
This research deals will the declared production planning operation in the general company of planting oils, which have great role in production operations management who had built mathematical model for correct non-linear programming according to discounting operation during raw materials or half-made materials purchasing operation which concentration of six main products by company but discount included just three products of raw materials, and there were six months taken from the 1st half of 2014 as a planning period has been chosen . Simulated annealing algorithm application on non-linear model which been more difficulty than possible solution when imposed restric
... Show MoreMultiple sclerosis (MS) is a neurodegenerative disorder with various clinical types. Glial fibrillary acidic protein (GFAP) is significantly elevated in the cerebrospinal fluid (CSF) of MS patients compared with that of healthy controls. The aim of this study is to evaluate serum levels of GFAP in relation to disease activity in relapsing-remitting MS patients and to compare them with those of healthy controls. Method: This study involved 58 MS patients of relapsing-remitting MS (RRMS) type, 22 in an active stage of the disease and 36 in remission, and 50 healthy individuals as age- and sex-matched controls. Blood samples were taken from the patients at the MS Clinic of the Baghdad Teaching Hospital, and the serum levels of GFAP were determ
... Show MoreHas influenced technology contact on social values in the Arab community in general and
the Iraqi community in particular whether this positive change or a negative effect on the level
of the individual and society is reflected this influence through socialization and human
relations as well as the behavior of individuals and dealing with others, where the world was
to soon pass changes slow, and the amounts that can be measured and trends can be calculated
and the effect of points can be identified and controlled, but the technological changes and
openness cognitive features that seemed evident in the last decade of the twentieth century
brought about a change in all scales and distances traditional considerations.<
Semi-parametric models analysis is one of the most interesting subjects in recent studies due to give an efficient model estimation. The problem when the response variable has one of two values either 0 ( no response) or one – with response which is called the logistic regression model.
We compare two methods Bayesian and . Then the results were compared using MSe criteria.
A simulation had been used to study the empirical behavior for the Logistic model , with different sample sizes and variances. The results using represent that the Bayesian method is better than the at small samples sizes.
... Show MoreThe support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample
... Show MoreIn this paper a new structure for the AVR of the power system exciter is proposed and designed using digital-based LQR. With two weighting matrices R and Q, this method produces an optimal regulator that is used to generate the feedback control law. These matrices are called state and control weighting matrices and are used to balance between the relative importance of the input and the states in the cost function that is being optimized. A sample power system composed of single machine connected to an infinite- bus bar (SMIB) with both a conventional and a proposed Digital AVR (DAVR) is simulated. Evaluation results show that the DAVR damps well the oscillations of the terminal voltage and presents a faster respo
... Show MoreThe issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the p
... Show MoreThe issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the proposed LAD-Atan estimator
... Show More