This work, deals with Kumaraswamy distribution. Kumaraswamy (1976, 1978) showed well known probability distribution functions such as the normal, beta and log-normal but in (1980) Kumaraswamy developed a more general probability density function for double bounded random processes, which is known as Kumaraswamy’s distribution. Classical maximum likelihood and Bayes methods estimator are used to estimate the unknown shape parameter (b). Reliability function are obtained using symmetric loss functions by using three types of informative priors two single priors and one double prior. In addition, a comparison is made for the performance of these estimators with respect to the numerical solution which are found using expansion method. The

The analysis of survival and reliability considered of topics and methods of vital statistics at the present time because of their importance in the various demographical, medical, industrial and engineering fields. This research focused generate random data for samples from the probability distribution Generalized Gamma: GG, known as: "Inverse Transformation" Method: ITM, which includes the distribution cycle integration function incomplete Gamma integration making it more difficult classical estimation so will be the need to illustration to the method of numerical approximation and then appreciation of the function of survival function. It was estimated survival function by simulation the way "Monte Carlo". The Entropy method used for the

Each phenomenon contains several variables. Studying these variables, we find mathematical formula to get the joint distribution and the copula that are a useful and good tool to find the amount of correlation, where the survival function was used to measure the relationship of age with the level of cretonne in the remaining blood of the person. The Spss program was also used to extract the influencing variables from a group of variables using factor analysis and then using the Clayton copula function that is used to find the shared binary distributions using multivariate distributions, where the bivariate distribution was calculated, and then the survival function value was calculated for a sample size (50) drawn from Yarmouk Ho

Picasso ceramics represented illuminated sign in ceramic art and excelled in accord ceramic art dimension aesthetically, and put it in a new prospects, despite the simplicity of the forms turn into a magical images and multiple interpretations.
So the search deliberately to choose purposive (37) samples divided into four groups, as follows: -
A flat shapes / palets or saucers / the vases /modified vases .
benefiting from indicators were spawned from literature ,to analyzing samples within the totals for the identification systems act forming art work`s:-
(1)Picasso's ceramic work product of a deliberate process represented a capacity of technical experience, and formal
(2)The system configuration in the ceramic art works c

This deals with estimation of Reliability function and one shape parameter (?) of two- parameters Burr – XII , when ?(shape parameter is known) (?=0.5,1,1.5) and also the initial values of (?=1), while different sample shze n= 10, 20, 30, 50) bare used. The results depend on empirical study through simulation experiments are applied to compare the four methods of estimation, as well as computing the reliability function . The results of Mean square error indicates that Jacknif estimator is better than other three estimators , for all sample size and parameter values

Idiomatic expressions in Russian journalism make one important borrowed means for making a dialogue with the receiver's intellect in so far as it has the distinct feature of  having clarity and exactness of meaning. The meaning is seen as a shortcut for covering a series of concepts and details so as to arrive at the intended meaning. This is done by stimulating the reader by the use of certain clear idioms. The use of such idioms in a journalistic text is not for a linguistic purpose only, but it is a cultural and social phenomenon reflecting the type of current changes in the society and it aims at discoursing with the reader's mind. This paper is a practi

      In this work, a weighted H lder function that approximates a Jacobi polynomial which solves the second order singular Sturm-Liouville equation is discussed. This is generally equivalent to the Jacobean translations and the moduli of smoothness. This paper aims to focus on improving methods of approximation and finding the upper and lower estimates for the degree of approximation in weighted H lder spaces by modifying the modulus of continuity and smoothness. Moreover, some properties for the moduli of smoothness with direct and inverse results are considered.

This study seeks to address the impact of marketing knowledge dimensions (product, price, promotion, distribution) on the organizational performance in relation to a number of variables which are (efficiency, effectiveness, market share, customer satisfaction), and seeks to reveal the role of marketing knowledge in organizational performance.

In order to achieve the objective of the study the researcher has adopted a hypothetical model that reflects the logical relationships between the variables of the study. In order to reveal the nature of these relationships, several hypotheses have been presented as tentative solutions and this study seeks to verify the validity of these hypotheses.

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function i