MMD010: Data Analysis: Finding Patterns With Regressions

Module code: MMD010

Module provider: International Business and Strategy; Henley Business School

Credits: 0

Level: Doctoral level

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: 2024/5

Available to visiting students:

Talis reading list:

Last updated: 28 May 2024

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 be also 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 student will be able to: 

1. An understanding of what OLS does and why we use regression analysis.   
2. An understanding 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, 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 includes both compulsory and optional lectures/tutorials. Attendance is required for compulsory lectures/tutorials. 

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

Students may observe the course with their first supervisor’s written consent, in which case they are exempted from assessment. 

Summative assessment

Penalties for late submission of summative assessment

This module is subject to the Penalties for late submission for Postgraduate Flexible programmes policy, which can be found at:

https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmissionpgflexible.pdf

The Module Convenor will apply the following penalties to work submitted late:

• where the piece of work is submitted up to one calendar month 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 one calendar month after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

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