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

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  • 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
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    References listed on IDEAS

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    1. Taha Zaghdoudi, 2013. "Bank Failure Prediction with Logistic Regression," International Journal of Economics and Financial Issues, Econjournals, vol. 3(2), pages 537-543.
    2. 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.
    3. Paul H. Kvam & Joel Sokol, 2004. "Teaching Statistics with Sports Examples," INFORMS Transactions on Education, INFORMS, vol. 5(1), pages 75-87, September.
    4. 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.
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