References
- Bhattacharyya HT, Kleinbaum DG, Kupper LL. Applied Regression Analysis and Other Multivariable Methods. J Am Stat Assoc [Internet]. 1979 Sep;74(367):
732 . Available from:https://www.jstor.org/stable/2287012?origin=crossref . - Kutner MH, Nachtsheim CJ, Neter J, Li W.
Applied linear statistical models . McGraw-Hill Location Chicago, IL; 2005. - Young DS. Handbook of Regression Methods. Handbook of Regression Methods; 2018. DOI: 10.1201/9781315154701
- Ernst AF, Albers CJ. Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions. PeerJ [Internet]. 2017 May 16;5:
e3323 . Available from:https://peerj.com/articles/3323 . - Jones L, Barnett A, Vagenas D. Common misconceptions held by health researchers when interpreting linear regression assumptions, a cross-sectional study. PLoS One [Internet]. 2025;20(6 JUNE):1–28. DOI: 10.1371/journal.pone.0299617
- Gelman A, Hill J. Data Analysis Using Regression and Multilevel/Hierarchical Models. Data Anal Using Regres Multilevel/Hierarchical Model. 2006 Dec 18. DOI: 10.1017/CBO9780511790942
- Osborne JW, Waters E. Four assumptions of multiple regression that researchers should always test. Pract Assessment, Res Eval. 2003;8(2):2002–3.
- Osborne JW, Waters E. Four assumptions of multiple regression that researchers should always test. Pract Assessment, Res Eval [Internet]. 2019 Nov 23 [cited 2022 Oct 12];8(1):
2 . Available from:https://scholarworks.umass.edu/pare/vol8/iss1/2 . - Best H, Wolf C. The Sage handbook of regression analysis and causal inference. 2015 [cited 2022 Dec 21]; Available from:
https://madoc.bib.uni-mannheim.de/40012/ . - Fox J. Regression diagnostics : an introduction; 2019.
- Weisberg S. Applied linear regression. Vol. 528. John Wiley & Sons; 2005. DOI: 10.1002/0471704091
- White H, MacDonald GM. Some large-sample tests for nonnormality in the linear regression model. J Am Stat Assoc. 1980;75(369):16–28. DOI: 10.1080/01621459.1980.10477415
- Williams MN, Grajales CAG, Kurkiewicz D. Assumptions of Multiple Regression: Correcting Two Misconceptions. Pract Assessment, Res Eval [Internet]. 2019 Nov 25 [cited 2022 Oct 11];18(1):
11 . Available from:https://scholarworks.umass.edu/pare/vol18/iss1/11 . - Casson RJ, Farmer LDM. Understanding and checking the assumptions of linear regression: A primer for medical researchers. Clin Exp Ophthalmol. 2014;42(6):590–6. DOI: 10.1111/ceo.12358
- Poole MA, O’Farrell PN. The Assumptions of the Linear Regression Model. Trans Inst Br Geogr. 1971;52(52):
145 . DOI: 10.2307/621706 - Jiang W, Chen H, Yang L, Pan X. moreThanANOVA: A user-friendly Shiny/R application for exploring and comparing data with interactive visualization. PLoS One. 2022 Jul 1;17(7 July). DOI: 10.1371/journal.pone.0271185
- Cobb P, McClain K. Principles of Instructional Design for Supporting the Development of Students’ Statistical Reasoning. Chall Dev Stat Literacy, Reason Think. 2004;(1997):375–95. DOI: 10.1007/1-4020-2278-6_16
- Doi J, Potter G, Wong J, Alcaraz I, Chi P. Web Application Teaching Tools for Statistics Using R and Shiny. Technol Innov Stat Educ. 2016;9(1). DOI: 10.5070/T591027492
- Jonas G. Programming and evaluation of Shiny applications for lectures; 2017.
- Satyahadewi N, Perdana H. Web Application Development for Inferential Statistics using R Shiny. Proc 1st Int Conf Math Math Educ (ICMMEd 2020). 2021;550(Icmmed 2020):425–9. DOI: 10.2991/assehr.k.210508.099
- Kallivokas D. Teaching basic statistic concepts to student classes with diverse mathematical background using specialized applets. Research methodology and approach Results. 2023;3(2):801–4. DOI: 10.25082/AMLER.2023.02.007
- González JA, López M, Cobo E, Cortés J. Assessing Shiny apps through student feedback: Recommendations from a qualitative study. Comput Appl Eng Educ. 2018;26(5):1813–24. DOI: 10.1002/cae.21932
- Chance B, Ben-Zvi D, Garfield J, Medina E. The Role of Technology in Improving Student Learning of Statistics. Technol Innov Stat Educ. 2007;1(1):1–24. DOI: 10.5070/T511000026
- American Statistical Association. Guidelines for assessment and instruction in statistics education: College report. Report [Internet]. 2005;(August 2005):1–61. Available from:
http://www.amstat.org/education/gaise/GaiseCollege_Full.pdf . - Fawcett L. Using Interactive Shiny Applications to Facilitate Research-Informed Learning and Teaching. J Stat Educ [Internet]. 2018;26(1):2–16. DOI: 10.1080/10691898.2018.1436999
- Schroeder CM, Scott TP, Toison H, Huang TY, Lee YH. A meta-analysis of national research: Effects of teaching strategies on student achievement in science in the United States. J Res Sci Teach. 2007;44(10):1436–60. DOI: 10.1002/tea.20212
- Lee HS, Linn MC, Varma K, Liu OL. How do technology-enhanced inquiry science units impact classroom learning? J Res Sci Teach. 2010;47(1):71–90. DOI: 10.1002/tea.20304
- Aljraiwi SS. The Effect of Classroom Web Applications on Teaching, Learning and Academic Performance among College of Education Female Students. J Educ Learn. 2017;6(2):
132 . DOI: 10.5539/jel.v6n2p132 - Laurent A, Lyu X, Kyveryga P, Makowski D, Hofmann H, Miguez F. Interactive Web-based Data Visualization and Analysis Tool for Synthetizing on-farm Research Networks Data. Res Synth Methods. 2021;12(1):62–73. DOI: 10.1002/jrsm.1440
- Waskom M. mwaskom/StatApps: Small web apps that illustrate statistical concepts [Internet]; 2024 [cited 2024 Jan 29]. Available from:
https://github.com/mwaskom/StatApps . - Amdur B. Regression Diagnostics: An Introduction [Internet]; 2024 [cited 2024 Jan 29]. Available from:
https://brettamdur.shinyapps.io/regDiagnostics/ . - Soetewey A. Simple linear regression [Internet]; 2024 [cited 2024 Jan 29]. Available from:
https://antoinesoetewey.shinyapps.io/statistics-202/ . - Nicholson J, Ridgway J, McCusker S. Getting Real Statistics into all Curriculum Subject Areas: Can Technology Make this a Reality? Technol Innov Stat Educ. 2013;7(2). DOI: 10.5070/T572013906
- Hughes LD, Lewis SA, Hughes ME. ExpressionDB: An open source platform for distributing genome-scale datasets. PLoS One. 2017 Nov 1;12(11). DOI: 10.1371/journal.pone.0187457
- Chang W, Cheng J, Allaire JJ, Sievert C, Schloerke B, Xie Y, et al. shiny: Web Application Framework for R [Internet]; 2022. Available from:
https://cran.r-project.org/package=shiny . - RStudio Team. RStudio: Integrated Development Environment for R [Internet]. Boston, MA; 2020. Available from:
http://www.rstudio.com/ . - R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria; 2022. Available from:
https://www.r-project.org/ . - Venables WN, Ripley BD. Modern Applied Statistics with S [Internet]. Fourth. New York: Springer; 2002. Available from:
https://www.stats.ox.ac.uk/pub/MASS4/ . - Fox J, Weisberg S. An {R} Companion to Applied Regression [Internet]. Third. Thousand Oaks {CA}: Sage; 2019. Available from:
https://socialsciences.mcmaster.ca/jfox/Books/Companion/ . - Wickham H. ggplot2: Elegant Graphics for Data Analysis [Internet]. Springer-Verlag New York; 2016. Available from:
https://ggplot2.tidyverse.org . - Wickham H, François R, Henry L, Müller K. dplyr: A Grammar of Data Manipulation [Internet]; 2022. Available from:
https://cran.r-project.org/package=dplyr . - Janssen P, De Pauw L, Mambretti M, Lara O, Walckiers J, Mackens L, et al. Characterization of the long-term effects of lethal total body irradiation followed by bone marrow transplantation on the brain of C57BL/6 mice. Int J Radiat Biol [Internet]. 2023;100(3):385–98. DOI: 10.1080/09553002.2023.2283092
- Eskandarani MA, Hau J, Kalliokoski O. Rapid ammonia build-up in small individually ventilated mouse cages cannot be overcome by adjusting the amount of bedding. Lab Anim (NY). 2023;52(6):130–5. DOI: 10.1038/s41684-023-01179-0
