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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2013/2014 -
- ARCHIVE as at 1 September 2013 for reference only
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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Statistical Communication Skills (MATH10070)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) Credits10
Home subject areaMathematics Other subject areaNone
Course website None Taught in Gaelic?No
Course descriptionCompulsory course for the Honours Degrees in Mathematics & Statistics and Economics & Statistics.

Use of the R language for statistical modelling and graphics, particularly for regression and one-way and two-way analysis of variance; writing and presenting statistics; project work.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Linear Statistical Modelling (MATH10005) AND Statistics (Year 2) (MATH08051)
Co-requisites Students MUST also take: Likelihood (MATH10004)
Prohibited Combinations Students MUST NOT also be taking Mathematical Computation & Communication Skills (MATH10006) OR Mathematical Computation & Communication Skills (Statistics) (MATH10056) OR Mathematical Computation & Communication Skills (Combined Degrees) (MATH10054) OR Data Analysis (MATH10011)
Other requirements None
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?No
Course Delivery Information
Delivery period: 2013/14 Semester 2, Available to all students (SV1) Learn enabled:  Yes Quota:  None
Web Timetable Web Timetable
Course Start Date 14/01/2014
Breakdown of Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 4, Seminar/Tutorial Hours 20, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 74 )
Additional Notes
Breakdown of Assessment Methods (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
No Exam Information
Summary of Intended Learning Outcomes
Ability to
1. use the R language to fit regression and analysis-of-variance models, to examine residuals from such models, and to investigate transformations of data
2. present a typeset report of a small statistical project
3. deliver an oral presentation
Assessment Information
See 'Breakdown of Assessment Methods' and 'Additional Notes' above.
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus Linear statistical models and residual analyses in the R language.
One-way analysis of variance.
Two-way analysis of variance with replication.
Two-way analysis of variance without replication.
Transferable skills Not entered
Reading list Montgomery, D.C., Design and Analysis of Experiments, Wiley, 2005.
Cox, D.R., Planning of Experiments, Wiley, 1958.
Krzanowski, W., An Introduction to Statistical Modelling, Arnold, 1998.
Study Abroad Not Applicable.
Study Pattern See 'Breakdown of Learning and Teaching activities' above.
KeywordsSCS
Contacts
Course organiserDr Bruce Worton
Tel: (0131 6)50 4884
Email: Bruce.Worton@ed.ac.uk		 Course secretaryMrs Kathryn Mcphail
Tel: (0131 6)50 4885
Email: k.mcphail@ed.ac.uk
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© Copyright 2013 The University of Edinburgh - 10 October 2013 4:52 am