module specification

This module will equip you with the mathematical and statistical skills that form a basis for all the Economics courses at the university. It will enable you to express social and business problems in numerical forms conducive to commercial and empirical analysis. You will learn how to apply a variety of quantitative methods via statistical software to generate solutions and insights. The two key focuses of the module will be on (1) a foundational level of data fluency and (2) key skills that you will apply both during your undergraduate degree and in your future career.

The aims of the module are:

- To understand how the real world can be expressed as data generating processes, the importance of their structure and how this can be compared to common statistical distributions.
- To learn how to graph and analyse data in a commercial context.
- To understand all the concepts and components of ordinary least squares (OLS) regression, testing for significance and potential problems.
- To apply OLS regression using an econometric software package.
- To introduce how to write an empirical research paper.
- To be able to use probability to calculate expected returns and expected outcomes.
- To be able to understand and draw conclusions from all the methods within the module.

Available for Study Abroad? YES

Introduction to data and testing. Comprehensive exploration of data types, including cross-sectional, time series, and panel data. Discussion of different statistical distributions. Students will gain insights into identifying the most suitable empirical methodologies based on dataset characteristics. A focus is placed on formulating hypotheses that address a problem or reflect the characteristics of the available data (LO1).

Commercial applications of data. A guide to the effective use of Excel equations and graphing functions for data analysis within a commercial context. Through exercises, participants will learn to extract meaningful insights from datasets relevant to real-world business scenarios (LO2).

Regression analysis. Hypothesis testing, defining a testable model as an equation, and OLS regression analysis constitute a significant portion of the module. Students will formulate empirically testable hypotheses and utilize econometric software for OLS regression. This will be accompanied with the elements appropriate to an empirical research paper, for example robustness checks to ensure the reliability of regression results. Participants will develop the ability to understand and evaluate the statistical significance of their findings (LO3, LO4).

Probability theory. This is introduced as a tool for expressing uncertainty in decision-making. Students will explore calculating expected returns and outcomes based on probability distributions, providing a foundation for making informed business decisions in uncertain environments (LO5).

Drawing conclusions. The purpose of quantitative analysis is the search for meaningful conclusions. Participants will synthesize their findings to answer questions in both commercial and academic contexts. This module will equip individuals with a foundational toolkit in applied economic and business analysis, enabling them to navigate and contribute to data-driven decision-making processes effectively (LO6).

Learning consists of ‘formal’ classroom learning directed by the teaching team, and reflective independent learning. The formal learning involves 12 weeks of 1 hour of lectures, and 2 hours of seminars where computer-based exercises will be attempted. Independent learning includes reading of the course material, and working on weekly exercises. Formal teaching will focus on application over theory.

Students will receive presentation slides and weekly exercises via Weblearn. Exercises will be used to develop student knowledge and where appropriate require use of Excel, Word and a statistical software package chosen by the module leader (e.g., eViews, R, etc). A portion of the weekly exercises will be reviewed in class. Formal teaching is to be accompanied by 7 hours a week of independent study. 30 hours are allocated to the completion of a summative coursework assessment.

Students will be grouped into action learning sets of 4-6 persons and will be given time during seminars to problem solve together. They will be encouraged to maintain a journal of experiences in order to reflect on their personal development.

On the completion of this module students will be able to:

1. Identify whether a dataset is cross sectional, time series or panel data. Understand different statistical distributions i.e., normal. Put forward appropriate hypotheses
2. Use Excel equations and graphing functions to analyse data in a commercial context.
3. Define an equation based model and select the correct empirical methodology to test the hypotheses.
4. Use an econometric software package to carry out an OLS regression that addresses the hypothesis, appropriate OLS tests and robustness checks. Evaluate the statistical significance of their results.
5. Use probability to express uncertainty, calculate expected returns and describe expected outcomes.
6. Draw commercial and academic conclusions based on the quantitative analysis of data.

Link will be provided at a later date. There is only one core reading:

Barrow, M. (2017) Statistics for economics, accounting and business studies, 7th
     ed., FT Prentice Hall. [This is an E-BOOK. Hard copies available at Holloway Road 519.502433 BAR]