دانلود رایگان مقاله انگلیسی رگرسیون خطی قدرتمند برای اطلاعات استحکام برشی نامقاوم - اشپرینگر 2018

Abstract.

Outlier data has attracted considerable interesting geotechnical data. When doing classical linear least squares regression, if the regression data satisfied certain regression weights, the ordinary least squares regression is considered as the best method. However, the estimating and regression results may be inaccurate in case of these data not meeting given assumptions. Particularly in least squares regression analysis, there is some data (outliers) violating the assumption of normally distributed residuals. Under situation of regression data blending to outliers, robust regression is the best fit method. It can discriminate outliers and offer robust results when the regression data exists outliers. The purpose of this study is to make use of robust regression method to trend regression in geotechnical data analysis. Without defining absolute outliers from geotechnical testing data, outlier data of undrained shear strength is detected based on robust regression result.

4 Conclusion

This paper demonstrates the RR regression analysis and LLR analysis in the case that outlier data existed in the su data. The regression analysis results show that RR method can deal with the outlier data in su data very well. The RR regression function can give a more desirable result than the LLR function. Usually, the RR regression function has a narrower confidence interval than the LLR regression function. It is highly recommended that RR regression analysis should be adopted in the case that there is some outlier data existing in the geotechnical data.