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

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

INFORMS Transactions on Education, 2022, vol. 23, issue 1, 1-11

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.

Keywords: logistic regression; OLS regression; all possible subsets; lasso; spreadsheets (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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