Module ASB-2111:
Statistical Methods

Module Facts

Run by Bangor Business School

20.000 Credits or 10.000 ECTS Credits

Semester 2

Organiser: Dr Alan Thomas

Overall aims and purpose

AIMS: The module will introduce students to the ideas of probability and statistical inference, and demonstrate the application of those ideas. The overall aim is to provide a good foundation in statistical methods and techniques. There is a strong focus on implementing these techniques in Microsoft Excel.

Course content

The course will include statistical techniques, which will include, sampling methods, hypothesis testing, regression and correlation analysis. It will also cover the use of robustness testing, and the analysing of time series and panel data. There will be instruction on data management and the use of the statistical tools within Excel.

Assessment Criteria

threshold

C- to C+ (50-59%): Much of the relevant information and skills mostly accurately deployed. Adequate grasp of theoretical/conceptual/practical elements. Fair integration of theory/practice/information in the pursuit of the assessed work's objectives. Some evidence of the use of creative and reflective skills.

good

B- to B+ (60-69%): Very good performance Most of the relevant information accurately deployed. Good grasp of theoretical/conceptual/practical elements. Good integration of theory/practice/information in pursuit of the assessed work's objectives. Evidence of the use of creative and reflective skills.

excellent

A- to A+ (70%+): Outstanding performance. The relevant information accurately deployed. Excellent grasp of theoretical/conceptual/practice elements. Good integration of theory/practice/information in pursuit of the assessed work's objectives. Strong evidence of the use of creative and reflective skills.

C- to C+

Learning outcomes

Apply key statistical concepts.
Understand sampling methods and interpret statistical inference.
Analyse data, and interpret the results.
Carry out investigations using summary statistics, graphs, techniques and exploratory data analysis, correlation and regression.
Formulate and test hypotheses.

Assessment Methods

Type	Name	Description	Weight
COURSEWORK	Group Data Analysis Report	Data analysis report to be done by groups of 4 or 5 students.The individual contribution to the group work will be assesed using the peer assesment tool WebPA.	20.00
EXAM	End of Semester 2 exam	End of module examination	80.00

Teaching and Learning Strategy

		Hours
Lecture	There will be a series of two-hour weekly lectures, which will include practical demonstrations and worked examples to try in class.	40
Workshop	2-hour drop-in session every other week. Attendance is non-compulsory.	20
Private study	Review and reflect upon the course material and practice the applications of techniques in Excel	140

Transferable skills

Literacy - Proficiency in reading and writing through a variety of media
Numeracy - Proficiency in using numbers at appropriate levels of accuracy
Computer Literacy - Proficiency in using a varied range of computer software
Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.
Presentation - Able to clearly present information and explanations to an audience. Through the written or oral mode of communication accurately and concisely.
Teamwork - Able to constructively cooperate with others on a common task, and/or be part of a day-to-day working team
Self-awareness & Reflectivity - Having an awareness of your own strengths, weaknesses, aims and objectives. Able to regularly review, evaluate and reflect upon the performance of yourself and others

Subject specific skills

Analysis, deduction and induction. Economic reasoning is highly deductive, and logical analysis is applied to assumption-based models. However, inductive reasoning is also important. The development of such analytical skills enhances students' problem-solving and decision-making ability.
Quantification and design. Data, and their effective organisation, presentation and analysis, are important in economics. The typical student will have some familiarity with the principal sources of economic information and data relevant to industry, commerce, society and government, and have had practice in organising it and presenting it informatively. This skill is important at all stages in the decision-making process.
Problem solving and critical analysis: analysing facts and circumstances to determine the cause of a problem and identifying and selecting appropriate solutions.
Numeracy: the use of quantitative skills to manipulate data, evaluate, estimate and model business problems, functions and phenomena.
Conceptual and critical thinking, analysis, synthesis and evaluation.
Self-management: a readiness to accept responsibility and flexibility, to be resilient, self-starting and appropriately assertive, to plan, organise and manage time.

Resources

Resource implications for students

Davis & Pecar Business Statistics using Excel Lee & Peters Business statistics using Excel & SPSS

Talis Reading list

http://readinglists.bangor.ac.uk/modules/asb-2111.html

Reading list