An Approach for Ushering Logistic Regression Early in Introductory Analytics Courses

Information Systems and Computing Academic Professionals. Box 488, Wrightsville Beach, NC 28480. e-mail: [email protected]; Web site: http://isedj.org

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12

Introductory Courses; Teaching Methods; Probability; Regression (Statistics); Statistics Education; Course Descriptions; Undergraduate Students; Business Administration Education; Course Content; Usability; Concept Formation; Comparative Analysis; Institutional Characteristics; Universities; Two Year Colleges; State Universities; Private Colleges; Goodness of Fit

1545-679X

Logistic regression (LoR) is a foundational supervised machine learning algorithm and yet, unlike linear regression, appears rarely taught early on, where analogy and proximity to linear regression would be an advantage. A random sample of 50 syllabi from undergraduate business statistics courses shows only two percent of the courses included LoR. Conceivable reasons for this dearth of LoR content is likely related to topic complexity, time constraints, and varying degrees of tool ease of use and support. We propose that these constraints can be countered by: [1] introducing logistic regression early, [2] informed tool selection prioritizing ease of use with comprehensive output, and [3] using/developing innovative, accessible, and easy to understand concept learning aids. This approach would leverage the proximity to linear regression and probability readily embed distributed practice for student understanding of a foundational technique.

As Provided

2023

EJ1363423