Literaturnachweis - Detailanzeige
| Autor/in | Brusco, Michael |
|---|---|
| Titel | Logistic Regression via Excel Spreadsheets: Mechanics, Model Selection, and Relative Predictor Importance |
| Quelle | In: INFORMS Transactions on Education, 23 (2022) 1, S.1-11 (11 Seiten)
PDF als Volltext |
| Zusatzinformation | ORCID (Brusco, Michael) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| Schlagwörter | Regression (Statistics); Spreadsheets; Data Analysis; Prediction; Graduate Students; Maximum Likelihood Statistics; Computation; Problem Solving; Least Squares Statistics; Measurement; Statistics Education |
| 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[subscript 1]-regularized logistic regression (lasso); and (iv) by illustrating the measurement of relative predictor importance using all possible subsets. (As Provided). |
| Anmerkungen | Institute for Operations Research and the Management Sciences (INFORMS). 5521 Research Park Drive Suite 200, Catonsville, Maryland 21228. Tel: 800-446-3676; Tel: 443-757-3500; Fax: 443-757-3515; e-mail: informs@informs.org; Web site: https://pubsonline.informs.org/journal/ited |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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