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CMPC2S12 - APPLIED STATISTICS B

Module Code:
CMPC2S12
Department:
Computing Sciences
Credit Value:
20
Level:
2
Organiser:
Prof Elena Kulinskaya
THIS MODULE IS RESERVED FOR ACTUARIAL SCIENCE AND BUSINESS STATISTICS STUDENTS. This is a module designed to give students the opportunity to apply statistical methods in realistic situations. While no advanced knowledge of probability and statistics is required, we expect students to have some background in probability and statistics before taking this module. The aim is to cover 2 topics from a list including: Estimating population abundance, Extremes and quartiles, Bootstrap methods and their application, Sample surveys, Simulations, Subjective statistics, Forecasting and Clustering methods. The topics on offer may vary to cover the interests of those in the class. In addition there will be two fixed topics, one on Regression and one on survival analysis.

Lecture notes, handouts and other materials will be made available via Blackboard during the course of the module.


No single text and students are expected to search the library stock for information.

Submission:

Written coursework should be submitted by following the standard CMP practice. Students are advised to refer to the Guidelines and Hints on Written Work in CMP.

Deadlines:

If coursework is handed in after the deadline day or an agreed extension:
 

 

Work submitted Marks deducted
After 15:00 on the due date and before 15:00 on the day following the due date 10 marks
After 15:00 on the second day after the due date and before 15:00 on the third day after the due date 20 marks
After 15:00 on the third day after the due date and before 15:00 on the 20th day after the due date.  All the marks the work merits if submitted on time (ie no marks awarded) 
After 20 working days Work will not be marked and a mark of zero will be entered


Saturdays and Sundays will NOT be taken into account for the purposes of calculation of marks deducted.

All extension requests will be managed through the LTS Hub. A request for an extension to a deadline for the submission of work for assessment should be submitted by the student to the appropriate Learning and Teaching Service Hub, prior to the deadline, on a University Extension Request Form accompanied by appropriate evidence. Extension requests will be considered by the appropriate Learning and Teaching Service Manager in those instances where (a) acceptable extenuating circumstances exist and (b) the request is submitted before the deadline. All other cases will be considered by a Coursework Coordinator in CMP.

For more details, including how to apply for an extension due to extenuating circumstances download Submission for Work Assessment (PDF, 39KB)
 

Plagiarism:

Plagiarism is the copying or close paraphrasing of published or unpublished work, including    the work of another student; without due acknowledgement. Plagiarism is regarded a serious offence by the University, and all cases will be investigated. Possible consequences of plagiarism include deduction of marks and disciplinary action, as detailed by UEA's Policy on Plagiarism and Collusion.


 


Module specific:

  • To provide an appreciation of statistical methods.
  • To give insight into the modelling process and inculcate a critical view of data analysis.

Transferable skills:

  • Exploratory data analysis
  • Statistical modelling
  • Report writing
  • Problem-solving

Subject specific:

  • Some understanding of current statistical methods
  • The ability to model data
  • An appreciation of exploratory data analysis

 


Total hours: 51 Lectures: 19 hours (with provisional weekly content)

  1. Exploratory data analysis (4 lectures)
  2. Regression (5 lectures)
  3. PCA and Factor analysis (5 lectures)
  4. Survival analysis (5 lectures)

Seminars: 28 hours (with provisional weekly schedule)

  1. Exploratory data analysis (6 seminars)
  2. Regression (6 seminars)
  3. PCA and Factor analysis (6 seminars)
  4. Survival analysis (6 seminars)

Laboratory work: 4 hours (with provisional weekly schedule) Exploratory data analysis


Coursework