An unbiased $C_{p}$ type criterion for ANOVA model with a tree order restriction

Inatsu, Yu

doi:10.32917/hmj/1499392825

Cited by 1 publication

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“…When the sample likelihood function under the constraints satisfies all three conditions simultaneously: having all the sample information, satisfying the constraints, and reflecting as much information on the parameters as possible, the likelihood function has a great value [26][27][28][29][30].…”

Section: Constructing the Sample Likelihood Functionmentioning

confidence: 99%

A digital teaching model for jurisprudence courses based on a linear regression model

Luo

2023

Applied Mathematics and Nonlinear Sciences

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To cultivate highly qualified legal talents, this paper designs a digital teaching model for jurisprudence courses based on a linear regression model to enhance the teaching effectiveness of jurisprudence courses. Regression modeling deals with the correlation between teaching variables and establishes a linear regression model in the design process. The sample likelihood function is constructed to output the teaching regression coefficients and the metrics of the model fitting effect, the raw data are dimensionless, and the dimensionless model covariance is calculated according to the nature of teaching expectations. On this basis, the digital teaching model of the jurisprudence course was explored to optimize digital teaching resources, adjust the structure, and establish a student evaluation mechanism. To verify the feasibility of this teaching model, its teaching effect was tested. The results showed that the digital teaching model of jurisprudence based on linear regression led to a teaching level of 2.6594%, and students’ mastery of knowledge reached 2.7952%. And the number of students with failing grades decreased by 480%, and the number of students receiving excellent grades increased by 3.53 times, with a teaching quality rating range of [3.95-4.39] points. It can be seen that the linear regression model promoted the jurisprudence course to break through the traditional teaching model and further improve the teaching quality of the jurisprudence course.

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Section: Constructing the Sample Likelihood Functionmentioning

confidence: 99%

A digital teaching model for jurisprudence courses based on a linear regression model

Luo

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

An unbiased $C_{p}$ type criterion for ANOVA model with a tree order restriction

Cited by 1 publication

References 0 publications

A digital teaching model for jurisprudence courses based on a linear regression model

A digital teaching model for jurisprudence courses based on a linear regression model

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