Online Statistical Modeling (Regression Analysis) for Independent Responses

International Conference on Mathematics: Education, Theory and Application

数学;教育

Made Tirta, I.^1 ; Anggraeni, Dian^1 ; Pandutama, Martinus^2

Jurusan Matematika FMIPA, Universitas Jember, Indonesia^1

Fakultas Pertanian, Universitas Jember, Indonesia^2

关键词: Additive models;    Command line interface;    Complex relationships;    Generalized additive model;    Markov Chain Monte-Carlo;    MCMC Regression;    Statistical modelling;    Web interface;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/855/1/012054/pdf
DOI  :  10.1088/1742-6596/855/1/012054

学科分类：发展心理学和教育心理学

来源: IOP

【 摘 要 】

Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.