پیش بینی مصرف هوازی مواد بر اساس مدل رگرسیون خطی / Aero-Material Consumption Prediction Based on Linear Regression Model

پیش بینی مصرف هوازی مواد بر اساس مدل رگرسیون خطی Aero-Material Consumption Prediction Based on Linear Regression Model

  • نوع فایل : کتاب
  • زبان : انگلیسی
  • ناشر : Elsevier
  • چاپ و سال / کشور: 2018

توضیحات

رشته های مرتبط آمار
گرایش های مرتبط آمار ریاضی
مجله پروسه علوم کامپیوتر – Procedia Computer Science
دانشگاه Naval Aviation University Qingdao Campus – PR China
شناسه دیجیتال – doi https://doi.org/10.1016/j.procs.2018.04.271
منتشر شده در نشریه الزویر
کلمات کلیدی انگلیسی aero-material consumption, linear regression model, parameter estimation, model test, residual analysis, prediction

Description

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.
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