MSc Business Analytics: Operational Research and Risk Analysis

The course provides a computer-based, application-oriented introduction to business statistics and forecasting with focus on core predictive models.

 

BMAN71791 Programme Req: BMAN71791 is only available as a core unit to students on MSc Business Analytics and as an elective to students on MSc Data Science (Business & Management pathway)

Undergraduate level Multivariate Calculus, Statistics and Algebra.
 

 

This course unit covers statistical analysis and modelling techniques with an emphasis on multivariate statistical applications in business and predictive analytics. We also consider time-series models. The aim of the course is to develop students' understanding of data analysis, statistical hypothesis testing and multivariate and predictive techniques.

 

At the end of the course unit, students should be able to:

•  Understand the fundamentals of basic statistical techniques and models.
•  Understand and design models for predictive analytics, particularly to be comfortable with multivariate linear regression.
•  Obtain hands-on experience with the statistical analysis software R and the R Studio interface, to perform basic statistical analyses.
 

 

Formal Contact Methods

Minimum Contact hours: 20 

Delivery format: Lecture and Workshops 

60% Exam

40% Coursework

 

•  Informal advice and discussion during a lecture, seminar, workshop or lab.
•  Online discussion forum.
•  Written and/or verbal comments on assessed or non-assessed coursework.
•  Generic feedback posted on Blackboard regarding overall examination performance.

Core Texts

Anderson, D. R. (Ed.). (2010). Statistics for business and economics (2nd ed.). Andover: South-Western Cengage Learning.

 Everitt, B. and Hothorn, T. (2011) An Introduction to Applied Multivariate Analysis with R . New York, NY, Springer New York. doi:10.1007/978-1-4419-9650-3.

 Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning : Data Mining, Inference, and Prediction  (Second Edition) Springer, New York. doi:10.1007/978-0-387-84858-7

Shumway, R. H. and Stoffer, D. S. (2017) Time series analysis and its applications with R examples (Fourth Edition). Cham, Switzerland, Springer. doi:10.1007/978-3-319-52452-8.


Additional texts

The books by Hadley Wickham (Chief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University, and creator of many of the main R libraries and the philosophies behind them -  http://hadley.nz/) are excellent. They are available free online e.g.
·    R for Data Science https://r4ds.had.co.nz/
·    ggplot2: Elegant Graphics for Data Analysis (Use R!) https://ggplot2-book.org/


You can also find well-informed answers to most technical queries you may have (with R or statistics) by googling! There are also very many other free resources and ‘how to’ examples on the web for data visualisation with R.
 
On the forecasting side, the book by Rob J Hyndman and George Athanasopoulos (Monash University, Australia) is excellent and its application and code uses R. It is free online:
·    Forecasting: Principles and Practice (2nd Edition) https://otexts.com/fpp2/

Husson, F. (2011) Exploratory multivariate analysis by example using R. Sébastien. Lê & Jérôme. Pagès. Boca Raton , CRC Press.
 
David Ray Anderson (2010) Statistics for business and economics (Second Edition). Andover, South-Western Cengage Learning.
 
Shoesmith E., Sweeney D., Anderson D., Williams T., et al. (2014) Statistics for business and economics. (Third Edition). Andover , Cengage Learning.
 
Hogg, R., McKean, J., and Craig, A. (2005) Introduction to Mathematical statistics. (Sixth Edition). Upper Saddle River, N.J, Prentice Hall.
 
Wooldridge, J.M. (2016) Introductory econometrics : a modern approach  (Sixth Edition). Boston, MA, Cengage Learning.
 

Informal Contact Methos

Office Hours

Email