تحليل التجارب ثنائية العوامل المتزنة وغير المتزنة لبيان اثر عاملين على بعض صفات محصول الشلب في العراق
Author name:
زينة ابراهيم حسن رشيد
Supervisor name:
كمال علوان خلف المشهداني | احمد شهاب احمد
General topic:
Administration and Economics
Specific topic:
Statistics
Degree:
Master
University:
University of Baghdad - Faculty Of Administration And Economics - Department Of Statistics
Language:
Arabic
University location:
Baghdad
First pages:
07T3696 - p.pdf
Abstract:
مما لاشك فيه ان التجربة العاملية (التي تهتم في وقت واحد وتجربة واحدة بدراسة عاملين فاكثر ),اذ ان لكل عامل مستويات ,وبتوافيق مستويات العوامل تتشكل المعالجات العاملية , هذه التجربة لها اهمية كبيرة في الجوانب التطبيقية وتتسم بالمزايا : - 1انها تسهم في تقلي | 1. It contributes to reduce the cost, time, efforts, and experimental units as a result of the implementation of a single experiment, rather than two or more experiments.2. Provide us by information of the main effects of factors and the effects of interactions of these factors.3. The possibility to compare all different combinations of two or factors to be studied.4. These experiments provide an opportunity to compare the levels of each factor separately, as if the experiment was devoted to him alone.Therefore, the information that we get from factorial experiments always be more perfect and realistic than those we get from single - factor experiments.The factorial experiments held by adopting equilibrium that is oriented common and natural , it might generated cases of unbalance ( what is allocated of the number of plots for each uneven factorial treatment), which may be done on purpose by the researcher (performing the experiment) or it may be due to lack of materials or resources, which leads to identify groups of processing that will be dealt with, or for other reasons like damage or loss pieces or results of experimental subject to certain processing. Such situations cause them a problem of how to analysis it, so the goal of this message is to research deeper theoretically to contribute in find solutions for the research problem and the vast knowing of the analysis methods of the balanced and unbalanced factorial experiments, with discussing the possibility of propose a method or technique to analysis this case. since been in separate theoretical aspect of this message addressed to several ways in addition to provide a proposed way for analysis. The theoretical beside the practical aspects has been improved (Chapter three) to take an advantage of a realistic data experiments (not analyzed) carried out with the Public Board for Agricultural Research (the party was made contract with) where the experiment has been analyzed (balanced and unbalanced factorials) to study the impact of the rice verities factor and also the distances of planting the rice for many characters of the rice crops, as well as study the impact factor rice varieties and planting dates factor on some of the qualities of the rice crop has implemented in Mashkhab station in the province of Najaf, and we used the methods of the analysis presented in addition to the proposed method. The conclusions have been reached regarding the moral of these factors and their interactions and to the possibilities that are available to use each method of analysis and the proposed method. The conclusions have been reached regarding the significant of these factors and their interactions and to the possibilities that are made available to use all method of methods of analysis and the suggested method was as as follows : 1 - perferred to use method frequencies the expected cell in the case of that the data is unbalanced and semi - proportional.2 - perferred to use method un weighted means in the case of that the data is unbalanced and disproportional.3 - perferred to use method of harmonic mean in the case of that the data is unbalanced and two case proportional and disproportional.4 - prefer to using the suggested method (median method ) in the event that the data is is unbalanced and disproportional.5 - prefer to data analysis unbalanced without trying to estimate missing values.
