IDEAS home Printed from https://ideas.repec.org/a/inm/orited/v23y2022i1p1-11.html
   My bibliography  Save this article

Logistic Regression via Excel Spreadsheets: Mechanics, Model Selection, and Relative Predictor Importance

Author

Listed:
  • Michael Brusco

    (Department of Business Analytics, Information Systems, and Supply Chain, Florida State University, Tallahassee, Florida 33206)

Abstract

Logistic regression is one of the most fundamental tools in predictive analytics. Graduate business analytics students are often familiarized with implementation of logistic regression using Python, R, SPSS, or other software packages. However, an understanding of the underlying maximum likelihood model and the mechanics of estimation are often lacking. This paper describes two Excel workbooks that can be used to enhance conceptual understanding of logistic regression in several respects: (i) by providing a clear formulation and solution of the maximum likelihood estimation problem; (ii) by showing the process for testing the significance of logistic regression coefficients; (iii) by demonstrating different methods for model selection to avoid overfitting, specifically, all possible subsets ordinary least squares regression and l 1 -regularized logistic regression (lasso); and (iv) by illustrating the measurement of relative predictor importance using all possible subsets.

Suggested Citation

  • Michael Brusco, 2022. "Logistic Regression via Excel Spreadsheets: Mechanics, Model Selection, and Relative Predictor Importance," INFORMS Transactions on Education, INFORMS, vol. 23(1), pages 1-11, September.
    • Handle: RePEc:inm:orited:v:23:y:2022:i:1:p:1-11
    DOI: 10.1287/ited.2021.0263
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/ited.2021.0263
    Download Restriction: no
    File URL: https://libkey.io/10.1287/ited.2021.0263?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Eric Huggins & Matt Bailey & Ivan Guardiola, 2020. "Case—Converting Point Spreads into Probabilities: A Case Study for Teaching Business Analytics," INFORMS Transactions on Education, INFORMS, vol. 21(1), pages 61-63, September.
    2. Eric Huggins & Matt Bailey & Ivan Guardiola, 2020. "Case Article—Converting NFL Point Spreads into Probabilities: A Case Study for Teaching Business Analytics," INFORMS Transactions on Education, INFORMS, vol. 21(1), pages 57-60, September.
    3. Taha Zaghdoudi, 2013. "Bank Failure Prediction with Logistic Regression," International Journal of Economics and Financial Issues, Econjournals, vol. 3(2), pages 537-543.
    4. Paul H. Kvam & Joel Sokol, 2004. "Teaching Statistics with Sports Examples," INFORMS Transactions on Education, INFORMS, vol. 5(1), pages 75-87, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David Almorza-Gomar & Rafael Ravina-Ripoll & Cristina Raluca Gh. Popescu & Araceli Galiano-Coronil, 2022. "Evaluation of an Experience of Academic Happiness through Football at University," IJERPH, MDPI, vol. 19(11), pages 1-13, May.
    2. M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 83-102, June.
    3. Ma. Bernadeth B. Lim & Hector R. Lim & Mongkut Piantanakulchai & Francis Aldrine Uy, 2016. "A household-level flood evacuation decision model in Quezon City, Philippines," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1539-1561, February.
    4. Ahmadian , Azam & Mahsa , Gorji, 2015. "Modeling of Banks Bankruptcy in Iran (Multivariate Statistical Analysis)," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 10(2), pages 1-24, January.
    5. Malik, Amina & Din, Shahab Ud & Shafiq, Muhammad & Butt, Babar Zaheer & Aziz, Haroon, 2019. "Earning Management and the Likelihood of Financial Distress in Banks — Evidence from Pakistani Commercial Banks," Public Finance Quarterly, Corvinus University of Budapest, vol. 64(2), pages 208-221.
    6. Abdus Samad, 2018. "How Early Can Non-Performance Loan Predict Bank Failure? Evidence from US Bank Failure during 2008-2010," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 9(1), pages 90-98, January.
    7. Arnab Adhikari & Indranil Biswas & Arnab Bisi, 2016. "Case Article—ABCtronics: Manufacturing, Quality Control, and Client Interfaces," INFORMS Transactions on Education, INFORMS, vol. 17(1), pages 20-25, September.
    8. Mohamed M. Khalifa Tailab, 2020. "Using Importance-Performance Matrix Analysis to Evaluate the Financial Performance of American Banks During the Financial Crisis," SAGE Open, , vol. 10(1), pages 21582440209, January.
    9. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
    10. Ma. Lim & Hector Lim & Mongkut Piantanakulchai & Francis Uy, 2016. "A household-level flood evacuation decision model in Quezon City, Philippines," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1539-1561, February.
    11. Kim-Hung Pho & Michael McAleer, 2021. "Specification and Estimation of a Logistic Function, with Applications in the Sciences and Social Sciences," Advances in Decision Sciences, Asia University, Taiwan, vol. 25(2), pages 74-104, June.
    12. M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Papers 1502.00882, arXiv.org.
    13. Yi-Shu Wang & Xue Jiang & Zhen-Jia-Liu, 2016. "Bank Failure Prediction Models for the Developing and Developed Countries: Identifying the Economic Value Added for Predicting Failure," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(9), pages 522-533, September.
    14. Liu, Zhen Jia, 2015. "Estudo cross-country sobre os fatores determinantes da crise financeira bancária," RAE - Revista de Administração de Empresas, FGV-EAESP Escola de Administração de Empresas de São Paulo (Brazil), vol. 55(5), September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orited:v:23:y:2022:i:1:p:1-11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.