## 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: 2019/0

Module Convenor: Dr Min Zou

Email: m.zou@henley.ac.uk

Type of module:

Summary module description:

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, selection, confounders.

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 observations. Generalizing from Regression Estimates: Confidence Interval, SE. External validity.

• Introduction to Causal Analysis (the counterfactual principle; reverse causality, confounders and selection in regressions).

• Introduction to Multiple Linear Regression Analysis. Comparing apples to apples.

• Interpretation of coefficients, including binary variables and interactions.

• Introduction to modelling probability: linear probability and logit models. Discussion: Observational data and causality.

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.

Contact hours:
 Autumn Spring Summer Lectures 10 Tutorials 9 Guided independent study: 81 Total hours by term 100 Total hours for module

Summative Assessment Methods:
 Method Percentage Written assignment including essay 100

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

Formative assessment methods:

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:

By assessment only in August/September.