تقييم المتغيرات المستقلة على الاستجابة الثنائية: الانحدار اللوجستي مقابل انحدار بروبيت في بيانات مرض السكري

[1] Washington, S., Karlaftis, M. G., & Mannering, F. L. (2003). Statistical and econometric methods for transportation data analysis. Chapman & Hall/CRC. DOI: https://doi.org/10.1201/9780203497111

[2] Rajendra, P., & Latifi, S. (2021). Prediction of diabetes using logistic regression and ensemble techniques. Computer Methods and Programs in Biomedicine Update, 1, 100032. https://doi.org/10.1016/j.cmpbup.2021.100032 DOI: https://doi.org/10.1016/j.cmpbup.2021.100032

[3] Aliman, G., Faye Nivera, T. S., Charmille Olazo, J. A., Jane Ramos, D. P., Danielle Sanchez, C. B., Amado, T. M., Arago, N. M., Jorda Jr., R. L., Virrey, G. C., & Valenzuela, I. C. (2022). Sentiment analysis using logistic regression.

[4] Han, S., & Lee, S. (2018). Estimation in a generalisation of bivariate probit models with dummy endogenous regressors. arXiv preprint arXiv:1808.05792. https://doi.org/10.48550/arXiv.1808.05792 DOI: https://doi.org/10.2139/ssrn.3233422

[5] Strzelecka, A., Kurdyś-Kujawska, A., & Zawadzka, D. (2020). Application of logistic regression models to assess household financial decisions regarding debt. Procedia Computer Science, 176, 3418–3427. https://doi.org/10.1016/j.procs.2020.09.055 DOI: https://doi.org/10.1016/j.procs.2020.09.055

[6] Jawa, T. M. (2022). Logistic regression analysis for studying the impact of home quarantine on psychological health during COVID-19 in Saudi Arabia. Alexandria Engineering Journal, 61(10), 7995–8005. https://doi.org/10.1016/j.aej.2022.01.047 DOI: https://doi.org/10.1016/j.aej.2022.01.047

[7] Abonazel, M. R., Dawoud, I., Awwad, F. A., & Tag-Eldin, E. (2023). New biased estimators for the probit regression model. Scientific Reports, 13(1), 5851. https://doi.org/10.1038/s41598-023-32452-4 DOI: https://doi.org/10.1016/j.sciaf.2023.e01565

[8] Srisathan, W. A., Ketkaew, C., Phonthanukitithaworn, C., & Naruetharadhol, P. (2023). Driving policy support for open eco-innovation enterprises in Thailand: A probit regression model. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), 100084. https://doi.org/10.1016/j.joitmc.2023.100084 ( DOI: https://doi.org/10.1016/j.joitmc.2023.100084

[9] Bhattacharyya, K. (1997). Key issues in polychotomous logit regression. Statistics in Medicine, 16(12), 1391–1397. https://doi.org/10.1002/(SICI)1097-0258(19970630)16:12<1391::AID-SIM540>3.0.CO;2-Y DOI: https://doi.org/10.1002/(SICI)1097-0258(19970630)16:12<1391::AID-SIM636>3.0.CO;2-1

[10] Bishai, D. (1996). Quality efficiency of health care providers: Inferences from a latent variable model. Health Economics, 5(2), 143–155. https://doi.org/10.1002/(SICI)1099-1050(199603)5:2<143::AID

[11] Alhemoud, A. M. (2011). An ordered probit regression model for estimating the effects of demographic factors on rice consumption in Saudi Arabia. International Journal of Business and Globalisation, 6(1), 1–17. https://doi.org/10.1504/IJBG.2011.037416 DOI: https://doi.org/10.1504/IJBG.2011.037322

[12] Obed, S. A., Mohammed, P. A., & Kadir, D. H. (2021). The estimation of (Covid-19) cases in Kurdistan region using Nelson Aalen estimator. Cihan University-Erbil Scientific Journal, 5(2), 24–31. https://doi.org/10.24086/cuesj.v5n2y2021.pp24-31 DOI: https://doi.org/10.24086/cuesj.v5n2y2021.pp24-31

[13] Kadir, D. H., & Amin, K. A. (2022). Comparing between Probit and Logit regression models to predict the factors affecting inflation in Iraq. Tikrit Journal of Administrative and Economic Sciences, 18(58, 2), 438–455. https://doi.org/10.25130/tjaes.18.58.2.25 DOI: https://doi.org/10.25130/tjaes.18.58.2.25

[14] Golam Kibria, B. M., & Saleh, A. K. M. E. (2012). Improving the estimators of the parameters of a probit regression model: A ridge regression approach. Journal of Statistical Planning and Inference, 142(6), 1421–1435. https://doi.org/10.1016/j.jspi.2011.12.023 DOI: https://doi.org/10.1016/j.jspi.2011.12.023

[15] Khaleel, Z. J., & Al-Jamil, S. K. (2021). The impact of some public finance variables on the gross national savings in Iraq. Tikrit Journal of Administrative and Economic Sciences, 17(54, 3), 430–443. https://doi.org/10.25130/tjaes.17.54.3.27 DOI: https://doi.org/10.25130/tjaes.17.54.3.27

[16] Salh, S. M., Abdalla, H. T., & Omer, Z. M. (2021). Using multinomial logistic regression model to study factors that affect chest pain. Tikrit Journal of Administrative and Economic Sciences, 17(53, 2), 534–555. https://doi.org/10.25130/tjaes.17.53.2.31 DOI: https://doi.org/10.25130/tjaes.17.53.2.31

[17] Sule, B. O., & Saporu, F. W. O. (2015). Mathematical theory and modeling. Journal of Mathematical Theory and Modeling, 5(10), 45-53.

[18] Hua, Y., Stead, T. S., George, A., & Ganti, L. (2025). Clinical risk prediction with logistic regression: Best practices, validation techniques, and applications in medical research. Academic Medicine & Surgery, 4(1), e131964. https://doi.org/10.62186/ams.v4i1.131964 DOI: https://doi.org/10.62186/001c.131964

[19] Dey, D., Haque, M. S., Islam, M. M., Aishi, U. I., Shammy, S. S., Mayen, M. S. A., Noor, S. T. A., & Uddin, M. J. (2025). The proper application of logistic regression model in complex survey data: A systematic review. BMC Medical Research Methodology, 25(1), 1–15. https://doi.org/10.1186/s12874-024-02454-5 DOI: https://doi.org/10.1186/s12874-024-02454-5

[20] Bewick, V., Cheek, L., & Ball, J. (2005). Statistics review 14: Logistic regression. Critical Care, 9(1), 112–118. https://doi.org/10.1186/cc3045 DOI: https://doi.org/10.1186/cc3045

[21] Gibbons, R. D., Hedeker, D., & Lab, B. (1994). Application of random-effects probit regression models. Journal of DOI: https://doi.org/10.1037/0022-006X.62.2.285

Consulting and Clinical Psychology, 62(2), 285–296. https://doi.org/10.1037/0022-006X.62.2.285 DOI: https://doi.org/10.1037/0022-006X.62.2.285