Multivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka

  • Siti Hadijah Hasanah Universitas Terbuka, Indonesia
Keywords: Basis function, GCV, Multivariate, Recursive, Splines


Multivariate Adaptive Regression Splines (MARS) used to model the active student’s status in the Department of Statistics at Universitas Terbuka and determine the factors that influence the response variable. This study consists of 9 variables, namely gender, age, education, marital status, job, initial registration year, number of registrations, credits, and GPA, but after modeling using the MARS method, the explanatory variable can affect the response variable is the initial registration year. Several registrations, GPA, and credits. Based on the results of the R output and using a 95% confidence interval, each base 1 to 10 function is partially significant with the p-value of the base 1-10 function being smaller than 0.05 and simultaneously with a smaller p-value. of 0.05, so that the above model has a significant effect partially or simultaneously on the response variable. From these results, it is concluded that the MARS model is suitable for determining the factors that affect the active status of students.


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How to Cite
Siti Hadijah Hasanah. (2021). Multivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka. Jurnal Matematika MANTIK, 7(1), 51-58.