| Code | QUA255 |
| School | EBSL |
| Module Level | 2 |
| Module Code | QUA255 |
| Class | GE|EL|MA|MI| |
| Availability | DE|SA| |
| Semester | 1 |
| Credits | 12 |
| ECTS Credits | 6 |
| Contact Hours | 48 |
| Self Study Hours | 72 |
| Course Aims | To inform a rational decision making process, business frequently employs objective analysis of a quantitative nature. The process of data analysis that is commonly employed in many business situations may be viewed as a three-stage process - collection, analysis and interpretation. Data must be collected in a manner that makes it as reliable as possible and free from bias. The analysis must be relevant to the type of data, employed correctly and the results interpreted correctly. In Understanding Business data (Level 1), students met a selection of the most elementary analytical and interpretive techniques. Here these studies are augmented by giving consideration to other well-used business quantitative techniques and also by addressing the issues involved in data collection. |
| Course Content | The listing indicates the subject matter dealt with during the Course. Some of the allocated time is used for computing laboratory exercises to deal with the many problems outside the range of classroom calculation capacity. These times are arranged as and when required.
The content covered in this module will endeavour to focus on the most widely utilised techniques in this area:
"Method of resource allocation (Linear Programming, Integer Programming, Transportation Problem, Dynamic Programming)
"Decision Analysis (Single and Multi-criteria decision making process including Analytic Hierarchy Process (AHP) and Goal Programming, Game Theory
"Issues in Data Collection Methodology. (Methods of collecting data. The Sample Survey. Questionnaire design and analysis including 2 test and Analysis of variance (ANOVA)
"Computer implementation of data presentation and analysis methods.
Core Skills ContentThe teaching and learning strategy has been designed to help students in developing the following skills
"Numeracy and quantitative skills - an ability to discriminate between different types of data and assign appropriate methodology to the analysis of the problem under consideration.
"Problem-solving and decision-making skills - an ability to implement suitable methodology for a given problem and present appropriate recommendations
"Presentation skills - an ability to present the results of a study in an appropriate form for the intended audience
"IT skills - an ability to implement the methodology considered via suitable IT software
"Research skills for dealing with data collection and analysis for business-related problems. |
| Learning Outcomes | A7, B5, B6, B7, B8, C6, C8, D8 |
| Presentation | The teaching and learning strategy incorporates a balance between classroom-based sessions, computer workshops and self-directed learning. The classroom sessions will be used to analyse key concepts in choosing and implementing data analysis techniques and allow detailed practical problems to be investigated. The computer workshops allow students to develop practical IT skills for solving problems. Additional problems will be provided for students to work on under self-direction. |
| Reading Recommended | Newbold, P. (2006) Statistics for Business and Economics, Prentice-Hall
Silver, M. (1997) Business Statistics, 2nd Edition, McGraw-Hill Publishing Company
Winston, W. (2003) Operations Research, Applications and Algorithms, Duxbury |
| Reading Required | Anderson, Sweeney and Williams (2004) An Introduction to Management Science - Quantitative Approaches to Decision Making, South Western College Publishing |
| Asseseement Methods | Assessment will be composed of three equally weighted parts. An end of semester examination will account for 33.33 % of the Total Module Mark. The remaining 66.67 % of the Total Module Mark will be provided by two coursework assignments set throughout the semester, highlighting different types of business data and selection of appropriate quantitative tools for analysis. |