دانلود رایگان مقاله انگلیسی پیش بینی مصرف هوازی مواد بر اساس مدل رگرسیون خطی - الزویر 2018

Abstract

It is indispensable to scientifically predict the consumption of aero-material spare parts and to make scientific decisions on aviation equipment maintenance resources and make full use of existing resources to improve maintenance capability. In this paper, the mathematic model and calculation method of linear regression model are introduced. And the parameter estimation and model test method of linear regression model is discussed. A linearization method is proposed for nonlinear problems. The application of linear regression model in forecasting the consumption of aero-material spare parts is analyzed by examples. Finally, the regression equation is analyzed for significance analysis, variance analysis and residual analysis. According to the analysis results, the regression equation is modified as necessary to further improve the prediction accuracy. The results show that the linear regression model is feasible and effective for the prediction of aero-material spare parts consumption.

4. Conclusions

The regression analysis and prediction method is a classical and practical forecasting scheme. It is precisely because of its classic, so it is also mature, and it is easier to understand, and more widely used. In the actual use process, if we choose specific methods and models, we can analyze the data in detail, and observe and analyze the scatters, which can also be more detailed and the prediction results will be satisfactory. When the regression prediction method is applied, it is necessary to determine whether there is a correlation between the variables. If there is no correlation between the variables, the regression prediction method of these variables will result in the wrong result. When the regression analysis is applied correctly, we should pay attention to the qualitative analysis of the relationship between the phenomena, avoid the extrapolation of the regression prediction and apply the appropriate data. At the same time, the linear regression prediction method proposed in this paper not only applies to the prediction of aero-material spare parts consumption, but also to other equipment indexes or parameters, which provides a scientific method and means for equipment support forecast.