Incorporating Multidimensional Data Analysis Methods for Probability Theory and Mathematical Statistics Teaching Reform and Practical Exploration

Abstract

Abstract Based on the new curriculum reform and big data technology, this paper uses the radial function and RBF neural network algorithm in the multidimensional data analysis method to obtain the center, variance and output layer power of the neurons of Probability Theory and Mathematical Statistics. Construct the teaching evaluation model of Probability Theory and Mathematical Statistics according to the RBF neural network algorithm and screen 25 secondary indicators from the three aspects of teachers, students, and course content, thus forming the teaching evaluation index system of Probability Theory and Mathematical Statistics. Determine the evaluators and evaluation methods, according to the specific implementation of the evaluation. The evaluation model of “Teaching Probability Theory and Mathematical Statistics” based on the RBF neural network is tested for reliability and validity. The results show that the evaluation values of 25 indicators in the indicator system of the RBF-based evaluation model for the teaching of Probability Theory and Mathematical Statistics are all out of the range of 8.010-9.0, and |u 1| = 16.392≥2.241, |u 2| = 10.052≥2.241 in the examination scores of Probability Theory and Mathematical Statistics from the second semester of the academic year 2018/2019 to the second semester of the academic year 2020/2021, i.e., the examination scores of the course for the five semesters as a whole do not obey a normal distribution. This study enables students to better master the theoretical knowledge of Probability Theory and Mathematical Statistics, which is of great significance to the educational reform and practical exploration of Probability Theory and Mathematical Statistics in colleges and universities.