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Session: 2022/23
Last modified: 10/01/2023 11:12:01
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Title of Module: Statistical Estimation and Inference |
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Code: MATH09012 |
SCQF Level: 9
(Scottish Credit and Qualifications Framework) |
Credit Points: 20 |
ECTS: 10
(European Credit Transfer Scheme) |
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| School: | School of Computing, Engineering and Physical Sciences |
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| Module Co-ordinator: | Laura
Stewart |
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| Summary of Module |
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This module extends the ideas in Statistics and probability from the level 8 module Probability and Statistics. The emphasis of the module is on survey sampling, point estimates and parametric and non-parametric hypothesis testing, specifically to prepare participants for research.
Simple random sampling from a population will be introduced, then extended to different sampling methods, followed by sample parameter estimation topics such as distribution of the mean and estimation of ratio.
Confidence intervals are reviewed and expanded from the level 8 module Probability and Statistics to include mean in normal population, point estimates and maximum likelihood estimation methods.
Hypothesis testing is introduced from first principles for parametric and non-parametric methods. The error types and p-values are discussed with respect to decision making.
Suitable statistical package(s) will be used for visual understanding of the concept, calculations and predictions.
The Graduate Attributes relevant to this module are given below:
- Academic: Critical thinker; Analytical; Inquiring; Knowledgeable; Problem-solver; Digitally literate; Autonomous.
- Personal: Effective communicator; Motivated; Resilient.
- Professional: Collaborative; Research-minded; Ambitious; Driven.
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| Module Delivery Method |
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| Face-To-Face | Blended | Fully Online | HybridC | HybridO | Work-based Learning |
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Face-To-Face
Term used to describe the traditional classroom environment where the students and the lecturer meet synchronously in the same room for the whole provision.
Blended
A mode of delivery of a module or a programme that involves online and face-to-face delivery of learning, teaching and assessment activities, student support and feedback. A programme may be considered “blended” if it includes a combination of face-to-face, online and blended modules. If an online programme has any compulsory face-to-face and campus elements it must be described as blended with clearly articulated delivery information to manage student expectations
Fully Online
Instruction that is solely delivered by web-based or internet-based technologies. This term is used to describe the previously used terms distance learning and e learning.
HybridC
Online with mandatory face-to-face learning on Campus
HybridO
Online with optional face-to-face learning on Campus
Work-based Learning
Learning activities where the main location for the learning experience is in the workplace.
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| Term(s) for Module Delivery |
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(Provided viable student numbers permit).
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| Term 1 | | Term 2 | | Term 3 | |
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| Learning Outcomes: (maximum of 5 statements) |
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On successful completion of this module the student will be able to:
L1.
Apply a range of sampling methods, distributions and perform parameter estimation.
L2.
Implement confidence interval estimations and perform relevant interpretation.
L3.
Perform appropriate hypothesis tests for parametric and non-parametric methods.
L4.
Use suitable computer software to perform and display appropriate analysis. |
| Employability Skills and Personal Development Planning (PDP) Skills |
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| SCQF Headings |
During completion of this module, there will be an opportunity to achieve
core skills in:
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| Knowledge and Understanding (K and U) |
SCQF Level 9.
Demonstrating a knowledge and understanding of concept of Sampling and basic methods of point estimates.
Demonstrating basic awareness of the application of statistical hypothesis, as appropriate, to the solution of problems.
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| Practice: Applied Knowledge and Understanding |
SCQF Level 9.
Using a range of standard techniques of decision making and the application of the hypothesis in research to solve standard statistical problems, as appropriate, and making valid interpretations of these. |
| Generic Cognitive skills |
SCQF Level 9.
Using a range of methods to analyse well-defined problems in relevant statistical contexts. |
| Communication, ICT and Numeracy Skills |
SCQF Level 9.
Conceptualising and analysing problems informed by professional and research issues.
Using suitable software to obtain, present and make valid interpretation of statistical problems and results, as appropriate.
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| Autonomy, Accountability and Working with others |
SCQF Level 9.
Working autonomously to produce short reports on statistical problems.
Collaborating with others in a small team to solve statistical problems.
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| Pre-requisites: |
Before undertaking this module the student should have
undertaken the following:
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Module Code:
MATH08010
| Module Title:
Probability and Statistics
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| Other: | or equivalent |
| Co-requisites | Module Code:
| Module Title:
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* Indicates that module descriptor is not published.
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| Learning and Teaching |
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Learning Activities
During completion of this module, the learning activities undertaken to
achieve the module learning outcomes are stated below:
| Student Learning Hours
(Normally totalling 200 hours):
(Note: Learning hours include both contact hours and hours spent on other learning activities) |
| Lecture/Core Content Delivery | 24 |
| Tutorial/Synchronous Support Activity | 12 |
| Laboratory/Practical Demonstration/Workshop | 12 |
| Independent Study | 146 |
| Personal Development Plan | 6 |
| 200
Hours Total
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**Indicative Resources: (eg. Core text, journals, internet
access)
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The following materials form essential underpinning for the module content
and ultimately for the learning outcomes:
“Statistical Estimation and Inference” class notes on the University VLE
"Introduction to Robust Estimation and Hypothesis Testing", RR Wilcox
Suitable software, e.g. Excel, SPSS, R and Word.
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(**N.B. Although reading lists should include current publications,
students are advised (particularly for material marked with an asterisk*) to
wait until the start of session for confirmation of the most up-to-date
material)
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| Engagement Requirements |
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In line with the Academic Engagement Procedure, Students are defined as academically engaged if they are regularly engaged with timetabled teaching sessions, course-related learning resources including those in the Library and on the relevant learning platform, and complete assessments and submit these on time. Please refer to the Academic Engagement Procedure at the following link: Academic engagement procedure |
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Supplemental Information
| Programme Board | Physical Sciences |
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| Assessment Results (Pass/Fail) |
No
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| Subject Panel | Physical Sciences |
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| Moderator | Kwok Chi Chim |
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| External Examiner | P Wilson |
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| Accreditation Details | |
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| Version Number | 1.05 |
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| Assessment: (also refer to Assessment Outcomes Grids below) |
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| Coursework worth 30% of the final mark. This will involve appropriate statistical analyses and use suitable software, as required. |
| Examination worth 70% of the final mark. |
(N.B. (i) Assessment Outcomes Grids for the module
(one for each component) can be found below which clearly demonstrate how the learning outcomes of the module
will be assessed.
(ii) An indicative schedule listing approximate times
within the academic calendar when assessment is likely to feature will be
provided within the Student Handbook.)
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Assessment Outcome Grids (Footnote A.)
Footnotes
A. Referred to within Assessment Section above
B. Identified in the Learning Outcome Section above
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Note(s):
- More than one assessment method can be used to assess individual learning outcomes.
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Schools are responsible for determining student contact hours. Please refer to University Policy on contact hours (extract contained within section 10 of the Module Descriptor guidance note).
This will normally be variable across Schools, dependent on Programmes &/or Professional requirements.
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| Equality and Diversity |
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The module is suitable for any student satisfying the pre-requisites.
UWS Equality and Diversity Policy |
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(N.B. Every effort
will be made by the University to accommodate any equality and diversity issues
brought to the attention of the School)
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