MMD010: Data Analysis: Finding Patterns With Regressions

Module code: MMD010

Module provider: International Business and Strategy; Henley Business School

Credits: 0

Level: NA

When you’ll be taught: Semester 1

Module convenor: Dr Min Zou, email: m.zou@henley.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: No placement specified

Academic year: 2026/7

Available to visiting students: No

Talis reading list: No

Last updated: 9 June 2026

Overview

Module aims and purpose

This module introduces theories and practices of data analysis that uncovers patterns in the data. It starts from basic concepts in statistical analysis and goes on to linear regressions with nonlinear functional forms. Important topics in data analysis such as multicolinearity, confounders and causality will also be covered.  

The module aims to broaden students’ understanding of data analysis by providing an overview of key methods and particularly focusing on regression analysis. 

STATA will be used as the statistical package in the module. 

Module learning outcomes

By the end of the module, it is expected that the students will be able to: 

1. An understanding of what OLS does and why we use regression analysis.   
2. An understanding of Stata OLS output.  
3. Have the ability to interpret cross section OLS estimates.   
4. Have some understanding of concepts of correlation, causality, multicollinearity, interaction

Module content

Use of statistical software to gain familiarity with basic statistics principles.  

1. Introduction to Regression Analysis. Understanding the structure of data, frequency and cross-tabulation. From scatterplot to OLS. Interpretation of coefficients. 
2. Running regressions, and measurement issues. Taking logs – percentage changes. Issues with working with real-life data; “outliers”-influential observations, confidence Interval.   
3. Introduction to Causal Analysis (reverse causality and multicollinearity in regressions).   Introduction to Multiple Linear Regression Analysis.   Interpretation of coefficients, including binary variables and interactions.   

Structure

Teaching and learning methods

The module will be taught through a series of lectures, PC lab-based tutorials, and self-directed study.

Study hours

At least 34 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

The module is non-credit bearing. Assessment on a Pass/Fail basis is based on Assignment (100%).

Summative assessment

Penalties for late submission of summative assessment

The following penalties for work submitted late will normally apply:  

Assessments with numerical marks 

• where the piece of work is submitted up to 30 calendar days after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark; 
• where the piece of work is submitted more than 30 calendar days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.  

Assessments marked Pass/Fail  

• where the piece of work is submitted within 30 calendar days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;  
• where the piece of work is submitted more than 30 calendar days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.  

Groupwork  

Where the work submitted late is a piece of groupwork submitted on behalf of the whole group, the penalty will apply to all members of the group. Individual contributions to groupwork submitted separately by each member will be subject to a late penalty only for the individual contributions that are late 

The University policy statement on penalties for late submission for Postgraduate Flexible programmes can be found at:  

https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmissionpgflexible.pdf?la=en&hash=8A89870FCA07250F482BFA8CD308588F  

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work. 

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Reassessment

Additional costs