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CMPC2S11 - STATISTICAL METHODS

Module Code:
CMPC2S11
Department:
Computing Sciences
Credit Value:
20
Level:
2
Organiser:
Dr. Aristidis K Nikoloulopoulos
This is a first course in statistics. It introduces the essential ideas of statistics deriving the necessary distribution theory as required. The aim of the course is to discuss the essential concepts in statistics rather than just to give a list of techniques for specific problems. The focus will be on developing ideas in distribution theory and inference based on the likelihood function. In consequence in addition to ideas of sampling and limit laws, it will cover maximum likelihood estimation and inference based on the likelihood ratio. If possible some Bayesian ideas will be introduced. While this course deals with concepts we shall aim to show how these are motivated by real problems

In lectures, handouts will be distributed for material that is difficult or lengthy to copy from the board e.g. derivations of formulae. These handouts will be available in the CMP Courseware folder. However, the handouts must not be considered as comprehensive, and students are expected to make their own notes during the lectures. It is expected that students will reinforce the material taught on the unit with their own reading (see book list in Library Resources below).

In seminars, students will be expected to tackle problems individually but with help available from the seminar  leader and one other teacher.


Required reading:

  • Wackerly, Mendenhall and Scheaffer, (Duxbury), Mathematical Statistics with Applications,
  • Montgomery and Runger, Applied Statistics and Probability for Engineers, (Wiley)

Optional reading:

  • Huff,D., How to Lie with Statistics, Penguin Books, (London.1991)
  • Moroney,M.J.  Facts from Figures (Viking Press, June 1952)


There are many, many good texts on statistics that cover this material available around class XA 1220 in the Library .A


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 appreciate the ideas which underly a range of statistical methods.
  • To apply these methods
  • To critically evaluate results
  • To be able to explain the results to non-specialist

Transferable skills:

  • Written communication
  • Oral communication
  • Model critique
  • To be able to analyse real-world problems using statistical thinking and techniques
  • To be able to make numerical inferences from data
  • Familiarity with common probability distributions that occur everywhere in scientific applications
  • Knowledge of common problems, pitfalls and fallacies in statistical analysis of data

Subject specific:

  • To be able to analyse real-world problems using statistical thinking and techniques
  • To be able to make numerical inferences from data
  • Familiarity with common probability distributions that occur everywhere in scientific applications
  • Knowledge of common problems, pitfalls and fallacies in statistical analysis of data

Total hours: 48

Lectures: 36 hours
 

  1. Random variables and distributions
  2. Multivariate distributions
  3. Covariance, correlation
  4. Moments, moment-generating functions
  5. Samples and sampling distributions
  6. Properties of Estimators
  7. Method of Moments and Maximum likelihood estimators.
  8. Basics of testing, definitions
  9. Neyman-Pearson Theory
  10. The likelihood ratio
  11. Goodness of fit
  12. Nonparametrics
  13. Bayes
     

Seminars: 12 hours

Laboratory work: 0 hours


This module is assessed by a combination of course test and an end of module examination. The exam accounts for 80% of the module's marks. It is scheduled during the summer exam period and lasts 3 hrs.

The course test is divided into two components of which is worth 10%.