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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Applied Statistics (MATH10096)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryMany standard statistical tests depend on the assumption that the population being sampled follows a parametric model. For instance, a t-test relies on the fact that our data distribution is (at least approximately) normally distributed. However, in many practical settings, the data distribution does not follow a simple form and there is a need for procedures that do not depend on a parametric model.

The course will focus on applied statistical techniques for analysing data and conducting hypothesis tests, including goodness-of-fit, permutation and nonparametric tests.
Course description In this course we will study a variety of statistical tests. These include:
1) Goodness of fit tests that assess whether the data are sampled from a prespecified distribution.
2) Permutation tests for comparing two or more populations, including the Mann-Whitney test, the Wilcoxon signed-rank test, the sign test and randomisation tests.
3) Nonparametric tests such as the runs test, the Kruskal-Wallis test.
We will also carry out the tests in R.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Statistics (Year 2) (MATH08051) OR Statistics (Yr 3) (MATH09022)
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesVisiting students are advised to check that they have studied the material covered in the syllabus of any pre-requisite course listed above before enrolling.
High Demand Course? Yes
Course Delivery Information
Academic year 2020/21, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 22, Seminar/Tutorial Hours 5, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 69 )
Assessment (Further Info) Written Exam 90 %, Coursework 10 %, Practical Exam 0 %
Additional Information (Assessment) Coursework 10%, Examination 90%
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)MATH10096 Applied Statistics2:00
Resit Exam Diet (August)Applied Statistics (MATH10096)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. formulate appropriate statistical hypotheses for scientific tests
  2. construct goodness of fit tests, permutation tests and other standard nonparametric tests
  3. distinguish between parametric and nonparametric tests, select the appropriate tests and apply them to a range of different forms of data
  4. carry out hypothesis tests in R and interpret the results accordingly
Reading List
Recommended, but not essential:

W. J. Conover, (1999), Practical Nonparametric Statistics, 3rd edition, Wiley.

J. Kloke and J. W. McKean, (2015), Nonparametric Statistical Methods Using R, CRC Press.

B. F. J. Manly, (1997), Randomisation, Bootstrap and Monte Carlo Methods in Biology,
Chapman & Hall.

I. P. Sprent and N.C. Smeeton, (2001), Applied Nonparametric Statistical Methods, 3rd
edition, Chapman & Hall.
Additional Information
Graduate Attributes and Skills Not entered
Course organiserDr Timothy Cannings
Course secretaryMr Christopher Palmer
Tel: (0131 6)50 5060
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