THE UNIVERSITY OF NEWCASTLE 
ECON2003: Predictive Modelling in Business 
Final Examination Guide and Notes 2026 
 
Part A: Revision Notes 
Part B: Sample Final Examination Paper 
Part C: Suggested Solutions to Final Exam Paper 
Part D: Final Examination - Hints and Tips 
 
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University of Newcastle

University of Newcastle - ECON2003: Predictive Modelling in Business

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ACADEMIC YEAR 2025 - 2026

University	University of Newcastle
Study Level	Undergraduate
Course Code	ECON2003
Course Name	Predictive Modelling in Business
Notes Title	Final Examination Guide and Notes
Format	PDF
Page Length	75

Get ready for Predictive Modelling in Business (ECON2003) at the University of Newcastle with this student-friendly guide. Designed for self-study from week one through to your final review, it explains concepts clearly and walks you through the full set of examinable topics covered in lectures. You’ll find practical guidance on the exam format, the types of questions to expect, and the high-priority ideas to focus on. Use it to reinforce class material, check your understanding with worked explanations, and streamline your revision. Keep it beside you as a quick reference to study smarter, build confidence, and improve your exam performance.

This course builds upon the first-year course (BUSN1010) through: (i) the development and application of quantitative methods and techniques; and (ii) the introduction of key concepts, models and issues in the classical linear regression model, as well as the introduction of recent developments of time-series econometrics and their applications to economics and finance. Accordingly, the course first provides a brief review of linear models; matrix algebra; rules of differentiation; constrained optimisation; integration and differential equations; and other related topics. This is followed by the introduction of the classical linear regression model; hypothesis testing; forecasting; issues in the violations of classical linear regression model. Other topics include: dynamic econometric models; simultaneous equation models; time-series econometrics; variance modelling; vector autoregressive models; and panel data models.

✔ Topics include:

Probability theory
The classical linear regression model (simple and multiple)
Logistic regression analysis
Violations of the assumptions of the classical linear regression model
Classification and Clustering
Time series econometrics

TOVO.

University of Newcastle - ECON2003: Predictive Modelling in Business

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