ETW2510 · Statistical modelling for decision making

Overview

This unit will introduce you to the empirical analysis of relationships between variables. The approach is based on linear regression theory and emphasises applied analysis of cross-sectional and time series data. Topics studied include properties of the least squares estimators, hypothesis testing, the choice of appropriate functional forms, model selection, dummy variables, theoretical and practical issues around analysing real-world data and the problems it poses to regression modelling.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. demonstrate the mechanism of Gauss Markov Theorem for Ordinary Least Square
2. evaluate regression models using global cross-sectional data to propose economic solutions
3. examine various Time Series model specifications in alignment with SDGs
4. compute the rigorous prediction and forecasting output with statistics analytical software.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Student experience, not official rules. Nothing here changes what the Handbook says.