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CMPSMC28 - APPLIED STATISTICS

Module Code:
CMPSMC28
Department:
Computing Sciences
Credit Value:
20
Level:
M
Organiser:
Prof Elena Kulinskaya
This is an applied statistics module designed to give Masters 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 done some background in probability and statistics before taking this course. The aim is to cover 4 topics from a list including: Estimating population abundance; Extremes and quantiles; Linear models; Bootstrap methods and their application; Sample surveys; Simulations; Subjective statistics (MCMC); Forecasing; and Clustering methods. If there is a demand for a specific topic we will always consider providing a project in that area if possible. The assessment will be by coursework.
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: 60

Lectures: 36 hours (with provisional weekly content)

  1. Introduction to R (9 lectures)  R project is a language and environment for statistical computing and graphics. Applied Regression Analysis (9 lectures) 
  2. Analysis of Variance and Covariance (9 lectures) 
  3. Survival analysis (9 lectures)

Laboratory work: 24 hours (with provisional weekly schedule)

  1. Introduction to R (6 labs) 
  2. Applied Regression Analysis (6 labs) 
  3. Analysis of Variance (6 labs) 
  4. Survival analysis (6 labs)

Coursework