مشخصات مقاله | |
انتشار | مقاله سال 2018 |
تعداد صفحات مقاله انگلیسی | 7 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
منتشر شده در | نشریه الزویر |
نوع مقاله | ISI |
عنوان انگلیسی مقاله | Aero-Material Consumption Prediction Based on Linear Regression Model |
ترجمه عنوان مقاله | پیش بینی مصرف هوازی مواد بر اساس مدل رگرسیون خطی |
فرمت مقاله انگلیسی | |
رشته های مرتبط | آمار |
گرایش های مرتبط | آمار ریاضی |
مجله | مجله علوم کامپیوتر پروسیدیا – Procedia Computer Science |
دانشگاه | Naval Aviation University Qingdao Campus – PR China |
کلمات کلیدی | مصرف هوازی، مدل رگرسيون خطي، برآورد پارامترها، مدل آزمون، تحليل باقيمانده، پيش بيني |
کلمات کلیدی انگلیسی | aero-material consumption, linear regression model, parameter estimation, model test, residual analysis, prediction |
شناسه دیجیتال – doi | https://doi.org/10.1016/j.procs.2018.04.271 |
کد محصول | E8193 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
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1. Introduction
The aero-material spare parts are essential material basis for aviation equipment maintenance engineering. With the development of aviation equipment, more and more complex equipment, maintenance of spare parts required for the variety and quantity are more and more spare parts financing, supply and storage process is also more complex. In this paper, we use linear regression method to forecast the consumption of spare parts [1]. The regression analysis and forecasting method is based on the analysis of the correlation between independent variables and dependent variables, the regression model between variables is built, and the regression model is used as the forecasting method. There are a lot of ways of regression analysis. Depending on the number of independent variables in the relationship the regression models can be divided into simple regression analysis and multivariate regression analysis. Depending on the correlation between independent variables and dependent variables, the regression models can be divided into linear regression forecasting and nonlinear regression forecasting. This paper focuses on simple linear regression prediction. 2. The principle of simple linear regression model The linear regression is a linear method used to simulate the relationship between one dependent variable and many explanatory variables. The case of one explanatory variable is called simple linear regression model, while the case of multiple explanatory variables is called multivariate linear regression model [2-4]. As a commonly used statistical methods and for its principles is clear, model is simple and easy to use, classical linear regression model has been a very wide range of applications in the aviation equipment maintenance and support. |