التحليل الإحصائي لإنتاج القمح المروي في سورية خلال الفترة 2000-2010
Abstract
كثيرة هي الأفكار التي تخطر على البال وكثيرة هي الأسئلة التي تتوارد إلى الذهن عندما يتعلق الأمر بتحليل ظاهرة ما باستخدام أساليب وأدوات تساعد في اتخاذ القرارات المناسبة.حاولنا في هذا البحث عرض أهمية علم الإحصاء وأهم أساليبه بالاعتماد على عدد المتغيرات الخاضعة للتحليل حيث تم استخدام: - المؤشرات الإحصائية الوصفية في حال كان لدينا متغير واحد - الأرقام القياسية في حال كان لدينا متغيران - التحليل العاملي وتحليل التباين في حال كان لدينا عدة متغيرات، وتم تطبيق هذه الأساليب على إنتاج محصول القمح المروي بنوعيه الطري والقاسي خلال الفترة 2000 – 2010 والعوامل المؤثرة في إنتاجه والمتمثلة في: المساحة المزروعة – المردود – الأسمدة– المياه- متوسط التكلفة – متوسط السعر، ومن ثم تم تحليل البيانات باستخدام الحزمة الإحصائية SPSS و برنامج Excel ، حيث تم تلخيص البيانات في عاملين رئيسين يفسران معاً 86.116%)) من التباين الكلي، عبر العامل الأول عن الأرض وما يتعلق بها من خلال مجموعة من المتغيرات هي المساحة المزروعة وما تحتاجه من أسمدة ومياه، أما العامل الثاني فقد عبر عن أهمية سعر مبيع الكيلو غرام من المحصول.
There are many ideas that come to the mind and many questions that keep coming to the mind when it comes to analyzing the phenomenon of using the methods and tools to help in the planning and making appropriate decisions.
In this research we tried to display the importance of statistics and the most important methods depending on the number of variables used in this analysis: descriptive statistical indicators in case we had one variable, indexes in case we had two variables, and factor analysis and analysis of variance in case we had several variables. These methods were applied to production of irrigated wheat crop, both types soft and hard, during the period 2000 - 2010 and the factors affecting its production, such as: the cultivated area, yield, fertilizers, water, the average cost and the average price. Then the data were analyzed using the Statistical package SPSS and program Excel. After that, they summarized in two main factors that justify together (86.116%) of the overall variance. The first factor was related to the land and everything related to it through a set of variables, which are: the cultivated area and the need to fertilizers and water., The second factor was the importance of the selling price of the crop per kilogram.
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