MMD010-Data Analysis: Finding Patterns With Regressions

Module Provider: International Business and Strategy
Number of credits: 0 [0 ECTS credits]
Level:NA
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2021/2

Module Convenor: Dr Min Zou
Email: m.zou@henley.ac.uk

Type of module:

Doctoral

Summary module description:

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. 

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

Aims:

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

Assessable learning outcomes:

By the end of the module students will be able to demonstrate: 

• An understanding of what OLS does and why we use regression analysis.  

• An understanding Stata OLS output 

• Have the ability to interpret cross section OLS estimates.  

• Have some understanding of concepts of correlation, causality, multicollinearity, interaction. 

Additional outcomes:

Outline content:

Use of statistical software to gain familiarity with basic statistics principles 

 

• Introduction to Regression Analysis. Understanding the structure of data, frequency and cross-tabulation. From scatterplot to OLS. Interpretation of coefficients.  

 

• Running regressions, measurement issues. Taking logs – percentage changes. Issues with working with real life data; “outliers”-influential observa tions, confidence Interval.  

 

• Introduction to Causal Analysis (reverse causality and multicollinearity in regressions).  

 

• Introduction to Multiple Linear Regression Analysis.  

 

• Interpretation of coefficients, including binary variables and interactions.  

 

Brief description of teaching and learning methods:

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

Summative assessment- Examinations:

The course in non-credit bearing. Assessment on a Pass/Fail basis is based on Assignment (100%) and is compulsory for Henley Business School PhD students taking the Qualitative Stream. 

Assignment can be submitted at any time up to the first Wednesday (inclusive) of the Spring Term of the academic year, and the assessment process will be completed within one month of submission. 

Summative assessment- Coursework and in-class tests:

N/A

Formative assessment methods:

N/A

Penalties for late submission:

The Module Convenor will apply the following penalties for work submitted late, in accordance with the University policy.  

• where the piece of work is submitted up to one calendar week after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for the piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days; 

 

• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded. 

The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf 

 

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. 

Assessment requirements for a pass:

A minimum mark of 50%

Reassessment arrangements:

Assignment by 1st October in the year the assessment is due. 

Additional Costs (specified where applicable):

Last updated: 20 May 2021