UNIVERSITY OF KENT
Confirmation that this version of the module specification has been approved by the School
Learning and Teaching Committee:
…15 th December 2014 …..…………………….(date)
SECTION 1: MODULE SPECIFICATION
1. Title of the module: Advanced Regression Modelling with R (MA594)
2. School or partner institution which will be responsible for management of the module
School of Mathematics, Statistics and Actuarial Science
3. Start date of the module: Autumn 2014, as MA579 (revised version start date as MA594,
September 2015)
4. The number of students expected to take the module: 5-10
5. Modules to be withdrawn on the introduction of this proposed module and consultation with other relevant Schools and Faculties regarding the withdrawal
MA579 - this module was introduced as part of the development of the MMathStat
Mathematics and Statistics programme but it has not yet run. It is being modified from Mlevel to H-level, by changing the level of the assessments.
6. The level of the module (e.g. Certificate [C], Intermediate [I], Honours [H] or Postgraduate
[M]): H
7. The number of credits and the ECTS value which the module represents: 15 (7.5 ECTS)
8. Which term(s) the module is to be taught in (or other teaching pattern): Autumn term
9. Prerequisite and co-requisite modules
Pre-requisite modules: MA629 Probability and Inference, MA632 Regression Models
There are no co-requisite modules.
10. The programmes of study to which the module contributes
MMathStat Mathematics and Statistics
11. The intended subject specific learning outcomes
On successful completion of this module students will: a) be proficient in the use of the statistical package R; b) be able to select suitable regression methods to analyse data in a sensible way and interpret the results appropriately;
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UNIVERSITY OF KENT c) be able to provide clear and competent reports on statistical analyses; d) have a systematic understanding of linear and generalized linear modelling, and be able to apply these techniques critically to real world data using R; e) be able to interpret the results of analyses, and communicate these clearly and concisely to other statisticians and to non-statisticians.
12. The intended generic learning outcomes
On successful completion of this module students will: a) be able to plan and implement the analysis of unfamiliar material in a professional way; b) be able to use information technology effectively for advanced data analysis; c) have enhanced their computational skills in statistical modelling; d) have developed a logical, mathematical approach to their work; e) be able to appropriately manipulate data for regression analysis; f) appreciate the need for techniques used to be appropriate to the type of data available.
13. A synopsis of the curriculum
R Package: This part will include a general introduction to the package and its components covering: linear models in R, writing your own functions in R, generalized linear models in
R.
Further linear regression: Model selection, collinearity, outliers and influential observations, polynomial regression.
Generalized Linear Model: Exponential family; Discrete data distributions: definition, estimation and testing; GLMs: estimation, model selection and model checking; Examples of GLMs: logistic regression and Poisson regression; Overdispersion;
14. Indicative Reading List
Crawley, M. .J. (2009). The R Book, Wiley.
Draper, N. R. and Smith, H. (1998), Applied Regression Analysis, 3 rd ed. Wiley.
Faraway, J. J. (2004). Linear Models with R, Chapman and Hall.
Faraway, J. J. (2006). Extending the Linear Model with R, Chapman and Hall.
McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, 2 nd ed, Chapman and
Hall.
15. Learning and Teaching Methods, including the nature and number of contact hours and the total study hours which will be expected of students, and how these relate to achievement of the intended module learning outcomes
Number of contact hours: approx. 36 (18 lectures, 18 computing classes)
Number of independent learning hours: approx.114
Total study hours: 150
The first part of the module will be based heavily on learning about R and the implementation of linear models in R. The second half will be mainly lectures looking at more advanced linear model theory and generalized linear models. These will be supported by computer classes. Students will also have the opportunity to solve exercises in which they have to select the appropriate approach to problems, which will assist in developing self-study skills.
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UNIVERSITY OF KENT
Achievement of module learning outcomes:
Lectures: 11(b)-(e), 12(f)
Exercises: 11(b)-(d), 12(d),(f)
Computing classes: 11(a), 11(d), 12(a)-(c),(e),(f)
16. Assessment methods and how these relate to testing achievement of the intended module learning outcomes
This module will be co-taught with parts of two M-level modules -- MA890 (Practical
Statistics and Computing) for R content, and MA882 (Advanced Regression Modelling) for regression content. However, a completely different pattern of assessments will be used for
MA584 and these assessments will be set at H-level.
The module will be assessed by 20% coursework (one assessment for R skills and one assessment for GLMs in R) and by 80% on a 2-hour computer assessment under exam conditions which will cover computing, application of linear and generalized linear models, interpretation of results, and underlying theoretical ideas.
Assessment of module learning outcomes:
R Assessment: 11(a),(c),(e), 12(c),(e)
GLM Assessment: 11(a)-(e), 12(a)-(c),(f)
Exam: 11(a),(b),(d), 12(b)-(d),(f)
17. Implications for learning resources, including staff, library, IT and space
The 18 hours of lectures are already taught in MA882 Advanced Regression Modelling. The
18 hours of computing classes will be taken from MA890 Practical Statistics and
Computing. Therefore, there will be no additional staffing or library implications.
18. The School recognises and has embedded the expectations of current disability equality legislation, and supports students with a declared disability or special educational need in its teaching. Within this module we will make reasonable adjustments wherever necessary, including additional or substitute materials, teaching modes or assessment methods for students who have declared and discussed their learning support needs. Arrangements for students with declared disabilities will be made on an individual basis, in consultation with the University’s disability/dyslexia support service, and specialist support will be provided where needed.
19. Campus(es) where module will be delivered: Canterbury
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