MG4F7 Half Unit
This information is for the 2018/19 session.
This course is compulsory on the MSc in Management (1 Year Programme). This course is not available as an outside option.
The course is designed to achieve an understanding of fundamental notions of data presentation and data analysis and to use statistical thinking in the context of business problems. The course deals with modern methods of data exploration (designed to reveal unusual or problematic aspects of databases), the uses and abuses of the basic techniques of inference, and the use of regression as a tool for management and for financial analysis.
15 hours of lectures and 15 hours of seminars in the MT.
Students will be expected to produce 1 project in the MT.
The formative will be an in-depth coursework, with two components to it. One component will help prepare participants for the Project (i.e., an application of a statistical technique to solve a company problem or research question), and the other component will help prepare participants for the Final Exam (i.e., a small problem set on which they will be tested).
• Statistics for Business Decision Making and Analysis, by Robert Stine and Dean Foster (S & F) second edition Pearson.
• An Essential Guide to Business Statistics by Dawn A. Willoughby April 2015
• Applied Business Statistics: Making Better Business Decisions, 7th Edition International Student Version
• Basic Econometrics, Oct 8, 2008 by Damodar Gujarati and Dawn Porter
• Multivariate Data Analysis (7th Edition), Joseph F. Hair Ronald L. Tatham Rolph E. Anderson William Black
Exam (50%, duration: 2 hours) in the summer exam period.
Project (35%, 3000 words) and class participation (15%) in the MT.
The coursework will be due in week 10 of MT
Total students 2017/18: 79
Average class size 2017/18: 15
Controlled access 2017/18: Yes
Lecture capture used 2017/18: Yes (MT)
Value: Half Unit
Personal development skills
- Team working
- Problem solving
- Application of information skills
- Application of numeracy skills
- Commercial awareness