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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.


Data: Plots and Summaries
Introduction to Probability
Statistical Inference: Confidence Intervals, Hypothesis Tests, and p-values
The Simple Linear Regression Model
The Multiple Linear Regression Model
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements For Business School PG students only, or by special permission of the School. Please contact the course secretary.
Course Delivery Information
Academic year 2020/21, 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 10, Feedback/Feedforward Hours 10, Revision Session Hours 3, Programme Level Learning and Teaching Hours 3, Directed Learning and Independent Learning Hours 104 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Individual coursework assessing all 4 learning outcomes.
Feedback Formative feedback is provided through weekly seminars and discussion board. Students are strongly encouraged to ask questions and participate in group discussions.
No Exam Information
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.

Wooldridge, J. (2013), Introduction to Econometrics: Europe, Middle East and Africa Edition, Cengage Learning.

Although the main text of the course is Statistical Techniques in Business and Economics, Introductory Econometrics is an excellent reference for regression analysis and an invaluable text for carrying out econometric applications and writing down your dissertation. This is the main text for Research Methods (compulsory course for some master programmes in semester 2). Please note that both books by Wooldridge are pratically the same textbook but the European version is less expensive.

Resource List:
Additional Information
Graduate Attributes and Skills Cognitive Skills:
The course will develop analytical, numerical and problem-solving skills.

Subject Specific Skills:
Students will gain an ability to understand and use statistics notation and theory to solve a wide range of problems in Finance.
Additional Class Delivery Information Students should devote 40 hours on understanding pre-recorded lectures and assigned readings (at least 4 hours per week). Students should engage with weekly materials (lecture notes, problems and exercises) before the scheduled seminar in the following week. Weekly seminars will explore and reinforce key themes from the weekly materials through a combination of discussion points, exercises, and QandAs. Course discussion board also allows students to ask questions outside of contact time. Student learning is best achieved if students engage with and reflect on any assigned problems/discussion points before weekly seminars. Students should actively seek feedback on their performance by engaging with weekly seminars and discussion board.
Course organiserDr Angelica Gonzalez
Tel: (0131 6)51 3027
Course secretaryMrs Kelly-Ann De Wet
Tel: (0131 6)50 8071
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