The reliability of optical sources is strongly dependent on the degradation and device characteristics are critically dependent on temperature. The degradation behaviours and reliability test results for the laser diode device (Sony-DL3148-025) will be presented .These devices are usually highly reliable. The degradation behaviour was exhibited in several aging tests, and device lifetimes were then estimated. The temperature dependence of 0.63?m lasers was studied. An aging test with constant light power operation of 5mW was carried out at 10, 25, 50 and 70°C for 100hours. Lifetimes of the optical sources have greatly improved, and these optical sources can be applied to various types of transmission systems. Within this degradation range,

This study discussed a biased estimator of the Negative Binomial Regression model known as (Liu Estimator), This estimate was used to reduce variance and overcome the problem Multicollinearity between explanatory variables, Some estimates were used such as Ridge Regression and Maximum Likelihood Estimators, This research aims at the theoretical comparisons between the new estimator (Liu Estimator) and the estimators

Item Difficulty and Item Discrimination Coefficient for School and College Ability Tests (SCAT) Advanced Form in Classical Test Theory (CTT) and Item Response Theory (IRT) and the Correlation among Them Mohammad moqasqas Haifa T. Albokai Assistant Professor of Measurement and Evaluation Associate Professor of Measurement and Evaluation College of Education, Taibah University The aim of this study was to study the item difficulty and item discrimination of the SCAT (advance form) with CTT, and IRT, and to study the correlation among them. To do this, the researchers used the data of their previous study, which conducted in (2011). It consisted of (3943) subject. Then, they used two-statistical programs (TAP, Bilog-MG-3) to obtain the item

In this paper, we made comparison among different parametric ,nonparametric and semiparametric estimators for partial linear regression model users parametric represented by ols and nonparametric methods represented by cubic smoothing spline estimator and Nadaraya-Watson estimator, we study three nonparametric regression models and samples sizes  n=40,60,100,variances used σ²=0.5,1,1.5 the results  for the first model show that N.W estimator for partial linear regression model(PLM) is the best followed the cubic smoothing spline estimator for (PLM),and the results of the second and the third model show that the best estimator is C.S.S.followed by N.W estimator for (PLM) ,the

Objective: To evaluate the clinical significance of open diagnostic testicular biopsy as prognostic predictor of
successful sperm retrieval among azoospermic infertile patients.
Design: Prospective study.
Setting: Infertility clinic and assisted reproduction unit at the institute of embryo research and infertility
treatment, Baghdad University.
Patients: Sixty infertile azoospermic patients.
Intervention: Pieces of testicular tissue taking during open diagnostic multiple bilateral testicular biopsies was
prepared for histological examination and grouped according to well-defined histopathological patterns.
Measurement of testicular size and serum reproductive hormones (FSH, LH, Testosterone, and PRL) were done

Statistical methods and statistical decisions making were used to arrange and analyze the primary data to get norms which are used with Geographic Information Systems (GIS) and spatial analysis programs to identify the animals production and poultry units in strategic nutrition channels, also the priorities of food insecurity through the local production and import when there is no capacity for production. The poultry production is one of the most important commodities that satisfy human body protein requirements, also the most important criteria to measure the development and prosperity of nations. The poultry fields of Babylon Governorate are located in Abi Ghareg and Al_Kifil centers according to many criteria or factors such as the popu

Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c