A methodology for developing regression analysis competencies among economics students

Abstract

This article presents a theoretical rationale and a structured stage-by-stage methodology for developing regression analysis competencies among undergraduate economics students. The relevance of the study is grounded in the persistent gap between students' technical proficiency in quantitative methods and their capacity for purposeful application of these methods in managerial decision-making contexts. Drawing on a competency-based approach and an analysis of the Bachelor's-level Educational and Professional Programme in Economics, the study establishes that regression analysis integrates mathematical, statistical, and applied dimensions of professional training, thereby necessitating a methodologically coherent instructional design.

The methodology encompasses three interrelated levels of training (mathematical, statistical, and applied) and ensures their didactic integration. A five-stage teaching framework is proposed, reflecting the logic of authentic analytical practice: problem formulation and hypothesis construction; preliminary data examination and descriptive analysis; model estimation and interpretation of output; residual analysis and verification of ordinary least squares assumptions; economic interpretation and formulation of managerial conclusions. The defining didactic condition of the framework is the orientation of each stage towards substantive inquiry rather than algorithmic reproduction, which transforms students' learning activity from reproductive to analytical. Competency attainment criteria are identified to distinguish between procedural knowledge of the method and its deliberate application in novel analytical situations. Directions for further research include the development of standardised diagnostic assessment instruments and empirical investigation of the effectiveness of integrating contemporary analytical environments into the economics curriculum.