Hari Purnomo Susanto (1), Ika Noviantari (2), Nely Indra Meifiani (3), Mega Isvanidiana Purnamasari (4), Tika Dedy Prasetyo (5), Mobinta Kusuma (6), Sumin Sumin (7)
Regression analysis is a statistical technique frequently employed across diverse disciplines utilizing SPSS. Users lacking a robust basis in regression and infrequently utilizing SPSS will experience anxiety. Anxiety will be intensified by the interpretation of the analysis results. This work aims to create user-friendly statistical software for regression analysis, requiring little configuration and capable of interpreting analytical results. This study employed the SCLD development model. This paradigm comprises five stages: Planning, Analysis, Design, Implementation, and System. The produced software is titled Simple Statistical Software series Regression Analysis, abbreviated as 3S-AR. The development yielded the 3S-AR program, which possesses functionality for regression analysis. The validation of development results was conducted by comparing the outcomes of 3S-AR analysis with those obtained from SPSS software. 3S-AR provides numerous advantages. Initially, it was user-friendly, and minimal configuration was required. A single analysis can present the outcomes of the regression analysis alongside all relevant assumptions. Secondly, the capability to offer an interpretation of the analytical findings. Third, if an analysis is incomplete, it can offer recommendations on the user's next steps. Fourth, it is complimentary. The development outcomes can facilitate users in conducting regression analysis with ease, particularly for individuals lacking a robust statistical background and proficiency in regression analysis tools.
Aljandali, A. (2017). Multivariate methods and forecasting with IBM® SPSS® Statistics. Springer International Publishing. https://doi.org/10.1007/978-3-319-56481-4
Amir, A. S., Tiro, M. A., & Ruliana. (2022). Development of R package for regression analysis with user friendly interface. ARRUS Journal of Mathematics and Applied Science, 2(1), 23–35. https://doi.org/10.35877/mathscience728
Andriani, S. (2017). Park test and breusch pagan godfrey test in detecting heteroscedasticity in regression analysis. Al-Jabar: Journal of Mathematics Education, 8(1), 63–72. https://doi.org/10.24042/ajpm.v8i1.1014
Chang, W. (2021). shinythemes: Themes for Shiny. https://cran.r-project.org/package=shinythemes
Chang, W., Cheng, J., Allaire, J. J., Xie, Y., & McPherson, J. (2015). shiny: Web Application Framework for R. R package version 0.11. 1. Retrieved February, 23.
Cohen. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge. https://doi.org/10.4324/9780203774441
Cunningham, J. B., & Aldrich, J. O. (2016). Using IBM SPSS statistics. In Aging (Vol. 2, Issue 3).
Davis, G., & Pecar, B. (2021). Statistics for business students: A guide to using Excel and IBM SPSS Statistics (First edit). Amazon Kindle Edition. https://www.stats-bus.co.uk
Dennis, A., Wixom, H. B., & Tegarden, D. (2015). Systems analysis design with UML Version 2.5: An Object-Oriented Approach. In John Wiley & Sons.
Domański, C., & Szczepocki, P. (2020). Comparison of selected tests for univariate normality based on measures of moments. Statistics in Transition New Series, 21(5), 151–178. https://doi.org/10.21307/stattrans-2020-060
Fox, J., & Weisberg, S. (2019). An {R} Companion to Applied Regression (Third). Sage. https://socialsciences.mcmaster.ca/jfox/Books/Companion/
Gross, J., & Ligges, U. (2015). nortest: Tests for Normality. https://cran.r-project.org/package=nortest
Hackenberger, B. K. (2020). R software: unfriendly but probably the best. Croatian Medical Journal, 61(1), 66–68. https://doi.org/10.3325/cmj.2020.61.66
Hadi, A. F., Sa'diyah, H., & Sumertajaya. (2017). Handling data non-normality in AMMI models with box-cox transformations (data non-normality on AMMI Models: Box-Cox Transformations). Journal of BASIC SCIENCE, 8(2).
Hebbali, A. (2024). olsrr: Tools for Building OLS Regression Models. https://cran.r-project.org/package=olsrr
Ho, R. (2013). Handbook of univariate and multivariate data analysis with IBM SPSS. Chapman and Hall/CRC. https://doi.org/10.1201/b15605
Idris, D. (2022). Conceptual understanding of linear regression among economics students at the university center of Tipaza, Algeria. Croatian Review of Economic, Business and Social Statistics, 8(2), 66–83. https://doi.org/10.2478/crebss-2022-0011
Lalonde, S. M. (2012). Transforming variables for normality and linearity – When, how, why and why not’s. In SAS Global Forum 2012.
Lee Pang, W. (2020). wleepang/DesktopDeployR. github. https://github.com/wleepang/DesktopDeployR
Leech, N., Barrett, K., & Morgan, G. A. (2013). SPSS for Intermediate Statistics. Routledge. https://doi.org/10.4324/9781410616739
Maziyya, P. A., Sukarsa, I. K. G., & Asih, N. M. (2015). Overcoming heteroskedasticity in regression using weighted least square. E-Journal of Mathematics, 4(1), 20. https://doi.org/10.24843/MTK.2015.v04.i01.p083
Nayebi, H. (2020). Advanced Statistics for Testing Assumed Casual Relationships. Springer International Publishing. https://doi.org/10.1007/978-3-030-54754-7
Niermann, S. (2007). Testing for linearity in simple regression models. AStA Advances in Statistical Analysis, 91(2), 129–139. https://doi.org/10.1007/s10182-007-0025-2
Nihayah, A. Z. (2019). Processing research data using SPSS 23.0 Software. UIN Walisongo Semarang.
Ogunleye, L, I., Oyejola, B, A., & Obisesan, K, O. (2018). Comparison of some common tests for normality. International Journal of Probability and Statistics, 7(5).
Olive, D. (2010). Multiple Linear and 1D Regression.
Osborne, J. W. (2010). Improving your data transformations: Applying the Box-Cox transformation. Practical Assessment, Research and Evaluation, 15(12).
Paura, L., & Arhipova, I. (2012). Advantages and disadvantages of professional and free software for teaching statistics. Information Technology and Management Science, 15(1). https://doi.org/10.2478/v10313-012-0001-z
Perrier, V., Meyer, F., & Granjon, D. (2023). shinyWidgets: Custom Inputs Widgets for Shiny (R package version 0.7.6). https://cran.r-project.org/package=shinyWidgets
R Core Team. (2023). R: A language and environment for statistical computing. https://www.r-project.org/
Schauberger, P., & Walker, A. (2023). openxlsx: Read, Write and Edit xlsx Files. https://cran.r-project.org/package=openxlsx
Susanto, H. P. (2016). Analysis of the relationship between anxiety, activity and achievement motivation with student mathematics learning outcomes. Beta Tadris Mathematics Journal, 9(2), 134. https://doi.org/10.20414/betajtm.v9i2.10
Tilley, S., & Rosenblatt, H. (2016). Systems analysis and design, Eleventh Edition. In A Guide to Medical Computing.
Trapletti, A., & Hornik, K. (2023). tseries: Time series analysis and computational finance. https://cran.r-project.org/package=tseries
Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (Fourth). Springer. https://www.stats.ox.ac.uk/pub/MASS4/
Wickham, H., & Bryan, J. (2023). readxl: Read Excel Files (R package version 1.4.2). https://cran.r-project.org/package=readxl
Zeileis, A., & Hothorn, T. (2002). Diagnostic checking in regression relationships. R News, 2(3), 7–10. https://cran.r-project.org/doc/Rnews/