Undergraduate Programme and Module Handbook 2018-2019 (archived)
Module MATH2041: STATISTICAL CONCEPTS II
Department: Mathematical Sciences
MATH2041:
STATISTICAL CONCEPTS II
Type |
Open |
Level |
2 |
Credits |
20 |
Availability |
Available in 2018/19 |
Module Cap |
|
Location |
Durham
|
Prerequisites
- Calculus and Probability I (MATH1061) and Linear Algebra I (MATH1071)
Corequisites
Excluded Combination of Modules
- Mathematics for Engineers and Scientists (MATH1551), Single
Mathematics A (MATH1561), Single Mathematics B (MATH1571)
Aims
- To introduce the main ideas, methods of statistics and statistical
computing, including a comparison of the Bayesian and frequentist
approaches.
Content
- Exploring data.
- Probability models.
- Bayesian inference.
- Frequentist inference.
- Likelihood methods.
- Goodness of fit and diagnostics.
- Non-parametric inference.
Learning Outcomes
- By the end of the module students will: be able to solve a
range of predictable and unpredictable problems in Statistical
Concepts.
- have an awareness of the abstract concepts of theoretical
mathematics in the Field of Statistical Concepts.
- have a knowledge and understanding of fundamental theories of
these subjects demonstrated through one or more of the following topic
areas: Finite population sampling.
- Confidence intervals and significance tests.
- Probability models, linear models, goodness of fit.
- Likelihood methods, maximum likelihood, Fisher's
information.
- Bayesian inference.
- Nonparametric methods.
- Statistical computing, using R.
- In addition students will have the ability to undertake and
defend the use of alternative mathematical skills in the following
areas with minimal guidance: Modelling.
Modes of Teaching, Learning and Assessment and how these contribute to
the learning outcomes of the module
- Lecturing demonstrates what is required to be learned and the
application of the theory to practical examples.
- Weekly homework problems provide formative assessment to guide
students in the correct development of their knowledge and
skills.
- Tutorials provide active engagement and feedback to the
learning process.
- The end-of-year examination assesses the knowledge acquired
and the ability to solve predictable and unpredictable
problems.
Teaching Methods and Learning Hours
Activity |
Number |
Frequency |
Duration |
Total/Hours |
|
Lectures |
42 |
2 per week for 21 weeks |
1 Hour |
42 |
|
Tutorials |
10 |
Fortnightly for 21 weeks |
1 Hour |
10 |
■ |
Problems Classes |
9 |
Fortnightly for 20 weeks |
1 Hour |
9 |
|
Computer Practicals |
10 |
Fortnightly for 20 weeks |
1 Hour |
10 |
■ |
Preparation and Reading |
|
|
|
129 |
|
Total |
|
|
|
200 |
|
Summative Assessment
Component: Examination |
Component Weighting: 100% |
Element |
Length / duration |
Element Weighting |
Resit Opportunity |
Written examination |
3 hours |
100% |
Yes |
One written assignment to be handed in every third
lecture in the first 2 terms. Normally each will consist of solving
problems from a Problems Sheet and typically will be about 2 pages long.
Students will have about one week to complete each
assignment.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University