MIS41130 Statistics &Simulation Methods

Academic Year 2019/2020

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

This course is designed to introduce students on the Business Analytics MSc programme to the essential techniques of probability and statistics, covering (broadly) probability, modelling, inference and simulation. The module will have a strong practical focus, with some programming. It assumes a prerequisite of some University-level statistics. The module will be delivered via a mix of lectures, tutorials and online lectures (blended learning)

Topics will include (but not limited to):
• Introduction to Probability
• Random variables and Descriptive Statistics Measures
• Discrete Distributions
• Continuous Distributions
• Inferential Statistics: Population vs. Sample, Sampling Distribution of the Sample Mean
• Confidence Intervals
• Hypothesis Testing
• Correlation and Regression
• Monte Carlo Simulation

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Curricular information is subject to change

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 describe common scenarios which can be modelled by them
• Describe on paper the machinery of null hypothesis testing for statistical significance, and execute it using a software library
• Describe common uses for correlation and regression
• For problems which can be addressed using simulation: map from a verbal problem description to a simulation approach to solving; generate random numbers drawn from appropriate distributions; build a simulation model; execute it and interpret the results statistically.

Indicative Module Content:

Topics will include (but not limited to):
• Introduction to Probability
• Random variables and Descriptive Statistics Measures
• Discrete Distributions
• Continuous Distributions
• Inferential Statistics: Population vs. Sample, Sampling Distribution of the Sample Mean
• Confidence Intervals
• Hypothesis Testing
• Correlation and Regression
• Monte Carlo Simulation

Student Effort Type Hours
Lectures

12

Tutorial

12

Autonomous Student Learning

120

Online Learning

24

Total

168

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.  
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: Examination 2 hour End of Trimester Exam No Graded No

60

Continuous Assessment: Continuous Assessment Varies over the Trimester n/a Graded No

40


Carry forward of passed components
Yes
 
Resit In Terminal Exam
Spring Yes - 2 Hour
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:
Weiss, N. A. (2012) Introductory statistics. Pearson. Boston. ISBN: 978-0321691224.
Winston, Wayne L. and Jeffrey B. Goldberg. (2004) Operations research : applications and algorithms. Thomson Learning. Belmont, Calif. ; London. ISBN: 0534423620

Highly recommended:
Ross, Sheldon M. (2018) A first course in probability. Pearson Education. Upper Saddle River, N.J. ISBN: 978-0134753119.
Ross, Sheldon M. (2006) Simulation. Elsevier Academic Press. Amsterdam ; London. ISBN: 9780125980630 :
0125980639

Good text:
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. (2017) An introduction to statistical learning : with applications in R. Springer. ISBN: 978-1461471370.

Background reading:
Ross, Sheldon M. (2009) Introduction to probability and statistics for engineers and scientists. Academic Press/Elsevier. Amsterdam, NL. ISBN: 9780080919379 (electronic bk.) 0080919375 (electronic bk.) 9780123704832 0123704839.
Robinson, Stewart. (2014) Simulation : the practice of model development and use. Wiley. Chichester. ISBN: 978-1137328021.






Name Role
Dr Annunziata Esposito Amideo Lecturer / Co-Lecturer
Mr Stefano Mauceri Tutor
Dr Miguel Nicolau Tutor
Autumn
     
Lecture Offering 1 Week(s) - 7, 9, 10, 11, 12 Mon 09:30 - 11:20
Lecture Offering 1 Week(s) - 9 Mon 12:00 - 13:50
Lecture Offering 1 Week(s) - Autumn: Weeks 7-12 Tues 09:30 - 11:20
Autumn
     

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