Otwarty dostęp

Conceptual understanding of linear regression among economics students at the university center of Tipaza, Algeria


Zacytuj

1. Agro, S. (1977). Graphing. USMES Intermediate “How to” Set. Available at https://files.eric.ed.gov/fulltext/ED220330.pdf [01 August 2022]. Search in Google Scholar

2. Akobeng, A. K. (2016). Understanding type I and type II errors, statistical power and sample size. Acta Paediatrica, Vol. 105, No. 6, pp. 605-609. DOI: 10.1111/apa.1338426935977 Open DOISearch in Google Scholar

3. Angrist, J. D., Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton university press. DOI: 10.2307/j.ctvcm4j72 Open DOISearch in Google Scholar

4. Angrist, J. D., Pischke, J. S. (2017). Undergraduate Econometrics Instruction: Through Our Classes, Darkly. Journal of Economic Perspectives, Vol. 31, No. 2, pp. 125-144. DOI: 10.1257/jep.31.2.125 Open DOISearch in Google Scholar

5. Bar-Hillel, M., Wagenaar, W. A. (1991). The perception of randomness. Advances in applied mathematics, Vol. 12, No. 4, pp. 428-454. DOI: 10.1016/0196-8858(91)90029-I Open DOISearch in Google Scholar

6. Batanero, C., Green, D. R., Serrano, L. R. (1998). Randomness, its meanings and educational implications. International Journal of Mathematical Education in Science and Technology, Vol. 29, No. 1, pp. 113-123. DOI: 10.1080/0020739980290111 Open DOISearch in Google Scholar

7. Batanero, C., Serrano, L. (1999). The meaning of randomness for secondary school students. Journal for Research in Mathematics Education, Vol. 30, No. 5, pp. 558-567. DOI: 10.2307/749774 Open DOISearch in Google Scholar

8. Ben-Zvi, D., Garfield, J. B. (Eds.). (2004). The challenge of developing statistical literacy, reasoning and thinking. Dordrecht, The Netherlands: Kluwer academic publishers. DOI: 10.1007/1-4020-2278-6 Open DOISearch in Google Scholar

9. Birnbaum, I. (1982). Interpreting statistical significance. Teaching Statistics, Vol. 4, No. 1, pp. 24-26. DOI: 10.1111/j.1467-9639.1982.tb00451.x Open DOISearch in Google Scholar

10. Boels, L., Bakker, A., Van Dooren, W., Drijvers, P. (2019). Conceptual difficulties when interpreting histograms: A review. Educational Research Review, Vol. 28, pp. 1-23. DOI: 10.1016/j.edurev.2019.100291 Open DOISearch in Google Scholar

11. Bossé, M., Marland, E., Rhoads, G., Rudziewicz, M. (2016). Searching for the Black Box: Misconceptions of Linearity. Chance, Vol. 29, No. 4, pp. 14-23. DOI: 10.1080/09332480.2016.1263094 Open DOISearch in Google Scholar

12. Capraro, M. M., Kulm, G., Capraro, R. M. (2005). Middle grades: Misconceptions in statistical thinking. School Science and Mathematics, Vol. 105, No. 4, pp. 165-174. DOI: 10.1111/j.1949-8594.2005.tb18156.x Open DOISearch in Google Scholar

13. Cooper, L. L., Shore, F. S. (2008). Students’ misconceptions in interpreting center and variability of data represented via histograms and stem-and-leaf plots. Journal of Statistics Education, Vol. 16, No. 2, pp. 1-13. DOI: 10.1080/10691898.2008.11889559 Open DOISearch in Google Scholar

14. Davidson, R., MacKinnon, J. G. (1993). Estimation and inference in econometrics. Available at https://russell-davidson.arts.mcgill.ca/textbooks/EIE-davidson-mackinnon-2021.pdf [13 May 2022]. Search in Google Scholar

15. Delmas, R., Garfield, J., Ooms, A. (2005, July). Using assessment items to study students’ difficulty reading and interpreting graphical representations of distributions. Available at https://www.causeweb.org/cause/archive/artist/articles/SRTL4_ARTIST.pdf [21 May 2022]. Search in Google Scholar

16. Doran, H. E., Doran, H. (1989). Applied regression analysis in econometrics. CRC Press. Search in Google Scholar

17. Escofier, B., Pagès, J. (1998). Analyses factorielles simples et multiples. Available at https://cdn-cms.f-static.com/uploads/1460418/normal_5b9ba5dc15394.pdf [10 September 2022]. Search in Google Scholar

18. Falk, R. (1986). Misconceptions of statistical significance. Journal of Structural Learning, Vol. 9, No. 1, pp. 83-96. Search in Google Scholar

19. Giordan, A., De Vecchi, G. (1987). Les origines du savoir. Des conceptions des apprenants aux concepts scientifiques. Delachaux et Nestlé, Neuchâtel-Paris. Search in Google Scholar

20. Glavic, B., Köhler, S., Riddle, S., Ludäscher, B. (2015). Towards Constraint-based Explanations for Answers and {Non-Answers}. Available at https://www.usenix.org/system/files/conference/tapp15/tapp15-glavic-revised.pdf [3 August 2022]. Search in Google Scholar

21. Gujarathi, D. M. (2004). Gujarati: Basic Econometrics. Available at http://portal.belesparadisecollege.edu.et:8080/library/bitstream/123456789/3407/1/10.Gujarat.PDF [25 June 2022]. Search in Google Scholar

22. Hancock, C. K. (1965). Some misconceptions of regression analysis in physical organic chemistry. Journal of Chemical Education, Vol. 42, No. 11, pp. 608-609. DOI: 10.1021/ed042p608 Open DOISearch in Google Scholar

23. Huang, J., Chen, T., Doan, A., Naughton, J. F. (2008). On the provenance of non-answers to queries over extracted data. Available at https://pages.cs.wisc.edu/~jhuang/case.pdf [17 May 2022].10.14778/1453856.1453936 Search in Google Scholar

24. Krishnan, S., Idris, N. (2014). Students’ misconceptions about hypothesis test. Redimat, Vol. 3, No. 3, pp. 276-293. DOI: 10.4471/redimat.2014.54 Open DOISearch in Google Scholar

25. Lebart, L., Morineau, A., Piron, M. (1995). Statistique exploratoire multidimensionnelle. Available at https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers11-10/010007837.pdf [7 June 2022]. Search in Google Scholar

26. Lee, S., Ludäscher, B., Glavic, B. (2018). Provenance summaries for answers and non-answers. Proceedings of the VLDB Endowment, Vol. 11, No. 12, pp. 1954-1957. DOI: 10.14778/3229863.3236233 Open DOISearch in Google Scholar

27. Lindner, T., Puck, J., Verbeke, A. (2020). Misconceptions about multicollinearity in international business research: Identification, consequences, and remedies. Journal of International Business Studies, Vol. 51, No. 3, pp. 283-298. DOI: 10.1057/s41267-019-00257-1 Open DOISearch in Google Scholar

28. Madsen, B. S. (2016). Data collection. In Statistics for Non-Statisticians, Springer, Berlin, Heidelberg, pp. 1-13. DOI: 10.1007/978-3-662-49349-6_1 Open DOISearch in Google Scholar

29. Motulsky, H. J. (2015). Common misconceptions about data analysis and statistics. Pharmacology research perspectives, Vol. 3, No. 1, pp. 1-8. DOI: 10.1002/prp2.93431722525692012 Open DOISearch in Google Scholar

30. Pfannkuch, M., Ben-Zvi, D. (2011). Developing teachers’ statistical thinking. In Teaching statistics in school mathematics-challenges for teaching and teacher education, Springer, Dordrecht, pp. 323-333. DOI: 10.1007/978-94-007-1131-0_31 Open DOISearch in Google Scholar

31. Reeves, C. A., Brewer, J. K. (1980). Hypothesis testing and proof by contradiction: an analogy. Teaching Statistics, Vol. 2, No. 2, pp. 57-59. DOI: 10.1111/j.1467-9639.1980.tb00387.x Open DOISearch in Google Scholar

32. Robert, A. D., Bouillaguet, A. (2002). L’analyse de contenu. Presses Universitaires de France, Paris. Search in Google Scholar

33. Rossman, A. J., Chance, B., Obispo, C. P. S. L. (2004). Anticipating and addressing student misconceptions. Available at https://www.rossmanchance.com/artist/proceedings/rossman.pdf [17 June 2022]. Search in Google Scholar

34. Sotos, A. E. C., Vanhoof, S., Van den Noortgate, W., Onghena, P. (2007). Students’ misconceptions of statistical inference: A review of the empirical evidence from research on statistics education. Educational research review, Vol. 2, No. 2, pp. 98-113. DOI: 10.1016/j.edurev.2007.04.001 Open DOISearch in Google Scholar

35. Sotos, A. E. C., Vanhoof, S., Van den Noortgate, W., Onghena, P. (2009). How confident are students in their misconceptions about hypothesis tests?. Journal of Statistics Education, Vol. 17, No. 2, pp. 1-21. DOI: 10.1080/10691898.2009.11889514 Open DOISearch in Google Scholar

36. Spanos, A. (1986). Statistical foundations of econometric modeling. Cambridge University Press. DOI: 10.1017/CBO9780511599293 Open DOISearch in Google Scholar

37. Swann, G. P. (2019). Is precise econometrics an illusion?. The Journal of Economic Education, Vol. 50, No. 4, pp. 343-355. DOI: 10.1080/00220485.2019.1654956 Open DOISearch in Google Scholar

38. Taber, K. S. (2005). Learning quanta: Barriers to stimulating transitions in student understanding of orbital ideas. Science Education, Vol. 89, No. 1, pp. 94-116. DOI: 10.1002/sce.20038 Open DOISearch in Google Scholar

39. Tompkins, C. A. (1993). Using and interpreting linear regression and correlation analyses: Some cautions and considerations. Available at http://aphasiology.pitt.edu/1435/1/21-04.pdf [10 April 2022]. Search in Google Scholar

40. Vallecillos, A. (2000). Understanding of the logic of hypothesis testing amongst university students. Journal für Mathematik-Didaktik, Vol. 21, No. 2, pp. 101-123. DOI: 10.1007/BF03338912 Open DOISearch in Google Scholar

41. Wild, C. J., Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International statistical review, Vol. 67, No. 3, pp. 223-248. DOI: 10.1111/j.1751-5823.1999.tb00442.x Open DOISearch in Google Scholar

42. Williams, M. N., Grajales, C. A. G., Kurkiewicz, D. (2013). Assumptions of multiple regression: Correcting two misconceptions. Practical Assessment, Research, and Evaluation, Vol. 18, No. 1, pp. 1-14. DOI: 10.7275/55hn-wk47 Open DOISearch in Google Scholar