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A diagrammatic representation of multiple regression model diagnostics for teaching and research/ Mahayaudin M. Mansor

This contribution features a flowchart representing a general procedure for modeling a response variable that is influenced by several explanatory variables. By modeling, the response values can be estimated based on the values of a set of explanatory variables referenced from literature or have pri...

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Main Author: M. Mansor, Mahayaudin
Format: Conference or Workshop Item
Language:English
Published: 2023
Subjects:
L Education (General)
Educational productivity
Online Access:https://ir.uitm.edu.my/id/eprint/91356/1/91356.pdf
https://ir.uitm.edu.my/id/eprint/91356/
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spelling my.uitm.ir.913562025-01-18T23:51:06Z https://ir.uitm.edu.my/id/eprint/91356/ A diagrammatic representation of multiple regression model diagnostics for teaching and research/ Mahayaudin M. Mansor M. Mansor, Mahayaudin L Education (General) Educational productivity This contribution features a flowchart representing a general procedure for modeling a response variable that is influenced by several explanatory variables. By modeling, the response values can be estimated based on the values of a set of explanatory variables referenced from literature or have priori with the response variable. However, multivariate regression is an intricate statistical procedure and can be intimidating to many, particularly students and some researchers from a non-statistics background. In addition, advances in data collection methods and digital information facilitate the availability of more exploratory variables, thus making the functional forms of regression more complex. Hence, the objective of the proposed flowchart is to visualize the multivariate regression procedure in a sequence of consolidated actions and processes focusing on diagnostics for multicollinearity, goodness-of-fit, and the underlying assumptions of normality, independence, homoscedasticity, and non-autocorrelation for the residuals. The other advantage is that the flowchart emphasizes not only quantitative but qualitative assessments prior to the establishment of a well-specified model. Consequently, the observed and estimated responses are plotted in a single plot, as many are unable to comprehend the discrepancies with the goodness-of-fit and error measures visually. Whether the response variable is best represented as either a linear or non-linear process, a well-specified model is desired to estimate values that match or are closer to the observed responses, producing more accurate outcomes. This flowchart covers the most important multivariate modeling steps and assumptions and, therefore, can be used as guided notes or a part of instructor-prepared handouts to promote active engagement during lectures, for independent study, or while programming related research. To my knowledge, a similar flowchart has yet to be produced. Besides patenting, there is a possibility to develop a fully automated R package for this work. 2023-10 Conference or Workshop Item PeerReviewed text en https://ir.uitm.edu.my/id/eprint/91356/1/91356.pdf A diagrammatic representation of multiple regression model diagnostics for teaching and research/ Mahayaudin M. Mansor. (2023) In: UNSPECIFIED.
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic L Education (General)
Educational productivity
spellingShingle L Education (General)
Educational productivity
M. Mansor, Mahayaudin
A diagrammatic representation of multiple regression model diagnostics for teaching and research/ Mahayaudin M. Mansor
description This contribution features a flowchart representing a general procedure for modeling a response variable that is influenced by several explanatory variables. By modeling, the response values can be estimated based on the values of a set of explanatory variables referenced from literature or have priori with the response variable. However, multivariate regression is an intricate statistical procedure and can be intimidating to many, particularly students and some researchers from a non-statistics background. In addition, advances in data collection methods and digital information facilitate the availability of more exploratory variables, thus making the functional forms of regression more complex. Hence, the objective of the proposed flowchart is to visualize the multivariate regression procedure in a sequence of consolidated actions and processes focusing on diagnostics for multicollinearity, goodness-of-fit, and the underlying assumptions of normality, independence, homoscedasticity, and non-autocorrelation for the residuals. The other advantage is that the flowchart emphasizes not only quantitative but qualitative assessments prior to the establishment of a well-specified model. Consequently, the observed and estimated responses are plotted in a single plot, as many are unable to comprehend the discrepancies with the goodness-of-fit and error measures visually. Whether the response variable is best represented as either a linear or non-linear process, a well-specified model is desired to estimate values that match or are closer to the observed responses, producing more accurate outcomes. This flowchart covers the most important multivariate modeling steps and assumptions and, therefore, can be used as guided notes or a part of instructor-prepared handouts to promote active engagement during lectures, for independent study, or while programming related research. To my knowledge, a similar flowchart has yet to be produced. Besides patenting, there is a possibility to develop a fully automated R package for this work.
format Conference or Workshop Item
author M. Mansor, Mahayaudin
author_facet M. Mansor, Mahayaudin
author_sort M. Mansor, Mahayaudin
title A diagrammatic representation of multiple regression model diagnostics for teaching and research/ Mahayaudin M. Mansor
title_short A diagrammatic representation of multiple regression model diagnostics for teaching and research/ Mahayaudin M. Mansor
title_full A diagrammatic representation of multiple regression model diagnostics for teaching and research/ Mahayaudin M. Mansor
title_fullStr A diagrammatic representation of multiple regression model diagnostics for teaching and research/ Mahayaudin M. Mansor
title_full_unstemmed A diagrammatic representation of multiple regression model diagnostics for teaching and research/ Mahayaudin M. Mansor
title_sort diagrammatic representation of multiple regression model diagnostics for teaching and research/ mahayaudin m. mansor
publishDate 2023
url https://ir.uitm.edu.my/id/eprint/91356/1/91356.pdf
https://ir.uitm.edu.my/id/eprint/91356/
_version_ 1823097775692185600
score 13.244413

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