Logistic Regression via Excel Spreadsheets: Mechanics, Model Selection, and Relative Predictor Importance
Published Online:9 Dec 2021https://doi.org/10.1287/ited.2021.0263
References
- Addinsoft (2021) XLSTAT (version 2021.2). Accessed April 30, 2021, https://www.xlstat.com/en/.Google Scholar
- (2016) Logistic regression analysis of predictors of loan defaults by customers of non-traditional banks in Ghana. Eur. Sci. J. 12(1):175–189. Google Scholar
- (1973) Information theory and an extension of the maximum likelihood principle. Petrov BN, Csaki BF, eds. Second Internat. Sympos. Inform. Theory (Academiai Kiado, Budapest), 267–281.Google Scholar
- (2015) Electronic word of mouth in social media: The common characteristics of retweeted and favourited marketer-generated content posted on Twitter. Internat. J. Internet Marketing Advertising 9(4):338–358. Crossref, Google Scholar
- (2001) Taxonomy for Learning, Teaching and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives (Longman, New York).Google Scholar
- (2003) The dominance analysis approach for comparing predictors in multiple regression. Psych. Methods 8(2):129–148.Crossref, Google Scholar
- (2009) Using dominance analysis to determine predictor importance in logistic regression. J. Ed. Behav. Statist. 34(3):319–347.Crossref, Google Scholar
- (2015) Developing logistic regression models using purchase attributes and demographics to predict the probability of purchases of regular and specialty eggs. British Poultry Sci. 56(4):1–11.Crossref, Google Scholar
- (1956) Taxonomy of Educational Objectives: The Classification of Educational Goals. Handbook I: Cognitive Domain (David McKay Company, New York).Google Scholar
- (2020) Imbalanced Classification with Python: Better Metrics, Balance Skewed Classes, Cost-Sensitive Learning (Machine Learning Mastery, San Juan, Puerto Rico).Google Scholar
- (2019) An Excel spreadsheet and VBA macro for model selection and predictor importance using all-possible-subsets regression. Spreadsheets in Education, 12(1). Accessed April 29, 2019, https://sie.scholasticahq.com/article/8064-an-excel-spreadsheet-andvba-macro-for-model-selection-and-predictor-importance-using-all-possible-subsetsregression.Google Scholar
- (1993) Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psych. Bull. 114(3):542–551. Crossref, Google Scholar
- (1998) Teaching logistic regression. Proc. Internat. Conf. Teaching Statist. (International Association for Statistical Education, Singapore), 284–289.Google Scholar
- (2013) Decision Analytics: Microsoft Excel (Que Publishing Company, Seattle).Google Scholar
- GAISE (2016) College report ASA revision committee. Guidelines for assessment and instruction in statistics education college report 2016. Accessed December 30, 2020, http://www.amstat.org/education/gaise.Google Scholar
- (1979) A tutorial on the SWEEP operator. Amer. Statist. 33(3):149–158. Google Scholar
- (2015) Variable importance in regression models. Wiley Interdisciplinary Rev. Comput. Statist. 7:137–152.Crossref, Google Scholar
- (1970) Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12(1):55–67.Crossref, Google Scholar
- (2013) Applied Logistic Regression, 3rd ed. (Wiley, New York).Crossref, Google Scholar
- (2020) Converting point spreads into probabilities: A case study for teaching business analytics. INFORMS Trans. Ed. 21(1):61–63.Abstract, Google Scholar
- IBM Corp. (2019) IBM SPSS Statistics for Windows, version 26.0. Accessed December 27, 2020. (IBM Corp., Armonk, NY).Google Scholar
- (2020) Applying the CRISP-DM framework for teaching business analytics. Decision Sci. J. Innovative Ed. 18(4):612–633. Crossref, Google Scholar
- (2004) Teaching statistics with sports examples. INFORMS Trans. Ed. 5(1):75–87.Link, Google Scholar
- (1980) Introduction to Bivariate and Multivariate Analysis (Scott, Foresman, Glenview, IL).Google Scholar
- (1973) Some comments on Cp. Technometrics 15(4):661–675.Google Scholar
- (2010) Logistic Regression: From Introductory to Advanced Concepts and Applications (Sage, Thousand Oaks, CA).Crossref, Google Scholar
- (2002) Subset Selection in Regression, 2nd ed. (Chapman and Hall, London).Crossref, Google Scholar
- (2007) Trashball: A logistic regression classroom activity. J. Statist. Ed. 15(1). Accessed December 30, 2020, https://www.tandfonline.com/doi/full/10.1080/10691898.2007.11889455.Google Scholar
- (2013) An Excel solver exercise to introduce nonlinear regression. Decision Sci. J. Innovative Ed. 11(3):263–278.Crossref, Google Scholar
- (2018) Spreadsheet Modeling and Decision Analysis, 8th ed. (Cengage, Boston).Google Scholar
- (1992) Introducing discriminant analysis to the business statistics curriculum. Decision Sci. 23(3):724–745.Crossref, Google Scholar
- (2013) How to do logistic regression in Excel. Accessed December 29, 2020, https://www.youtube.com/watch?v=rbKtZcrTlr8.Google Scholar
- (2018) Implementation of logistic regression algorithm for complaint text classification in Indonesian ministry of marine and fisheries. Internat. J. Comput. Techniques 5(5):74–78. Google Scholar
- (2017) Teaching logistic regression using ordinary least squares in Excel. 2017 JSM Proc. Papers Presented Joint Statist. Meetings (American Statistical Association, Baltimore), 2963–2987). Accessed December 29, 2020, http://www.statlit.org/pdf/2017-Schield-ASA.pdf.Google Scholar
- (1978) Estimating the dimension of a model. Ann. Statist. 6(2):461–464.Crossref, Google Scholar
- (1996) Regression shrinkage and selection via the Lasso. J. Roy. Statist. Soc. B. 58(1):267–288.Crossref, Google Scholar
- (2013) Bank failure prediction with logistic regression. Internat. J. Econom. Financial Issues 3(2):537–543.Google Scholar

