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MIS41130

Academic Year 2024/2025

Statistical Methods (MIS41130)

Subject:
Management Information Systems
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
8
Module Coordinator:
Dr Debajyoti Biswas
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Core (except for students holding a degree in statistics or similar)

This course is designed to introduce students on the MSc Business Analytics programme to the essential techniques of probability and statistics, covering (broadly) probability theory, descriptive and inferential statistics. The module will have a practical focus, with real-like applications and use of software. It assumes a prerequisite of some University-level statistics. The module will be delivered via a mixture of face-to-face lectures and tutorials as well as online material.

Topics can include (but are not limited to):
• Probability theory
• Descriptive statistics measures
• Discrete random variables
• Continuous random variables
• Sampling concepts
• Sampling distribution of the sample mean
• Confidence intervals
• Hypothesis testing
• Correlation and regression

About this Module

Learning Outcomes:

At the end of this module, students should be able to:
• Define and explain the basic laws of probability, including Bayes’ theorem
• Describe several common distributions and scenarios which can be modelled by them
• Describe on paper the machinery of hypothesis testing for statistical significance and execute it using a software
• Describe common uses for correlation and regression
• Given a data set, be able to describe it and analyse results arising from the application of inferential statistics techniques

Indicative Module Content:

Topics can include (but are not limited to):
• Probability theory
• Descriptive statistics measures
• Discrete random variables
• Continuous random variables
• Sampling concepts
• Sampling distribution of the sample mean
• Confidence intervals
• Hypothesis testing
• Correlation and regression

Student Effort Hours:
Student Effort Type Hours
Lectures

12
Tutorial

12
Autonomous Student Learning

120
Online Learning

24
Total

168

Approaches to Teaching and Learning:
Lectures, problem-solving, tutorials

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Group Work Assignment: This is a group assessment which requires students to demonstrate their understanding of statistics as well as their ability to analyze and interpret real-world data. Week 12 Alternative linear conversion grade scale 40% No

50

 No
Exam (In-person): Main Written Exam (in-person) covering all topics in the module. End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No

50

 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Essential Reading:
Lindley, D. V. and Scott, W. F. (1995). New Cambridge Statistical Tables (2nd Edition). Cambridge University Press. ISBN: 0521484855, 9780521484855
Weiss, N. A. (2012). Introductory statistics. Pearson. Boston. ISBN: 978-0321691224.

Highly recommended:
Ross, S. (2018). A first course in probability. Pearson Education. Upper Saddle River, N.J. ISBN: 978-0134753119.

Good text:
Gareth, J., Witten, D., Hastie T., and Tibshirani, R. (2017). An introduction to statistical learning : with applications in R. Springer. ISBN: 978-1461471370.
Garrett G. and Hadley W. (2016). R for Data Science. Publisher(s): O'Reilly Media, Inc. ISBN: 9781491910399.

Name Role
Dr Annunziata Esposito Amideo Lecturer / Co-Lecturer
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