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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
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryCompulsory course for the Honours Degrees in Mathematics & Statistics and Economics & Statistics. Students must have a strong background in statistics and have previously taken Linear Statistical Modelling (MATH10005) AND Statistics (Year 2) (MATH08051), and be taking Likelihood (MATH10004). The workshops will involve application of statistical methods covered in these statistics courses, as well as other models introduced in the course.
Course description 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.

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.
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
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2015/16, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
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 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Coursework 100%
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Ability to 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. Be able to apply the theory in other statistics courses to challenging problems.
  3. Ability to analyse data and interpret results of statistical analyses.
  4. Ability to present a typeset report of an individual statistical project.
  5. Ability to deliver an oral presentation.
Reading List
Recommended, not essential:

(1) Montgomery, D.C., Design and Analysis of Experiments, Wiley, 2005.
Montgomery, D.C., Design and Analysis of Experiments, Wiley, 2005. ISBN: 9781118097939

(2) Cox, D.R., Planning of Experiments, Wiley, 1958. 92.95 ISBN: 9780471574293 New Edition in Print (1958 edition out of print)

(3)Krzanowski, W., An Introduction to Statistical Modelling, Arnold, 1998.
31.50 ISBN: 9780470711019 New Edition
Additional Information
Graduate Attributes and Skills Not entered
Study Abroad Not Applicable.
Course organiserDr Bruce Worton
Tel: (0131 6)50 4884
Course secretaryMr Thomas Robinson
Tel: (0131 6)50 4885
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