In this paper, the multivariate linear statistical method is
applied to research the undergraduate grades of students from the school of mathematics
in Hefei University of Technology, and explore the impact on the later achievement by
the early stage of achievement from all undergraduate courses. First, we get the main
components from the previous courses by principal component analysis, then construct a
linear regression model between the later achievement and main components by the stepwise
regression method. Next, a linear regression model between the later achievement and the
early stage of achievement from all undergraduate courses is constructed by Adaptive-Lasso
method. Finally, comparative analysis is performed for the result of the above models. The
research shows that the principal component regression model based on the Adaptive-Lasso
method can well fit the later achievement, and give a reasonable explanation for the later
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TAN Changchun, ZHANG Xuelian, HU Junying. The Statisitical Analysis of Undergraduate Grades Based on the Adaptive-Lasso. CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST, 2016, 32(5): 541-550.