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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Statistics For Finance (CMSE11086)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits15 ECTS Credits7.5
SummaryThis course will provide you with the statistical concepts needed for financial applications. The goal is to apply statistical tools to analyse data, solve problems and make business decisions. This introductory course will provide you with the essential background for subsequent courses.
Course description The content of this course is similar to an advanced undergraduates statistics course. Although there is a strong emphasis on theory, you will get an introduction to an econometric software for conducting basic empirical research. The material is presented to understand, rather than memorise, statistical concepts. The course shall be accessible for both, students with strong quantitative background, and those who are ready to put effort into the class material.

Outline content

- Data: Plots and Summaries
- Introduction to Probability
- Probability
- Statistical Inference: Confidence Intervals, Hypothesis Tests, and p-values
- The Simple Linear Regression Model
- The Multiple Linear Regression Model

Student Learning Experience

This course is taught via a combination of weekly lectures and tutorials. Students will be introduced to an econometrics software (either STATA or SAS) during computer-based tutorials. Tutorials are intended to help students go over exercises they found difficult rather than to solve all the weekly tutorial questions. Therefore, students should attempt solving tutorial questions in advance of tutorial sessions. Students will receive early feedback by taking a mid-term exam. By the end of the course an individual project, based on STATA or SAS, which assesses the achievement of intended learning outcomes will be delivered.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2024/25, Available to all students (SV1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 150 ( Lecture Hours 20, Seminar/Tutorial Hours 9, Summative Assessment Hours 4, Programme Level Learning and Teaching Hours 3, Directed Learning and Independent Learning Hours 114 )
Assessment (Further Info) Written Exam 100 %, Coursework 0 %, Practical Exam 0 %
Additional Information (Assessment) 40% Mid-course exam (Individual) - Assesses all course Learning Outcomes

60% Final exam (Individual) - Assesses all course Learning Outcomes
Feedback Formative: feedback will be provided throughout the course.

Summative: Feedback will be provided on the assessments within agreed deadlines.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)Statistics For Finance (CMSE11086): Final Exam2:00
Outwith Standard Exam Diets NovemberStatistics For Finance (CMSE11086): Mid-Course Exam2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Define, explain and illustrate the concepts of probability, random variables, point estimation, interval estimation, hypothesis testing and inference.
  2. Critically discuss the link of theory with empirical applications.
  3. Understand and critically evaluate the importance of assumptions in statistics/econometrics.
  4. Carry out basic data analysis in a statistical software package.
Reading List
Douglas A. Lind, William G Marchal, Samuel A. Wathen (2012), Statistical Techniques in Business and Economics, 15th Edition, McGraw-Hill

Wooldridge, J. (2015), Introductory Econometrics: A Modern Approach, 6th edition, Thomson.

Resource List:
Additional Information
Graduate Attributes and Skills Communication, ICT, and Numeracy Skills

After completing this course, students should be able to:

Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.

Cognitive Skills

After completing this course, students should be able to:

Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to
KeywordsNot entered
Course organiserDr Angelica Gonzalez
Tel: (0131 6)51 3027
Course secretaryMiss Lindsay Hunter
Tel: (0131 6)50 3823
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