MATHS 7025 - Research Methods and Statistics
North Terrace Campus - Semester 2 - 2023
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General Course Information
Course Details
Course Code MATHS 7025 Course Research Methods and Statistics Coordinating Unit Mathematical Sciences Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact 3 hours per week Available for Study Abroad and Exchange N Incompatible STATS 7053 Restrictions Not available to MMaSc students. Assessment Examination, Statistics assignment, Research Methods assignment Course Staff
Course Coordinator: Anthony Mays
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
Students should- Be aware of their responsibilities as research students, including scientific ethics, and data and code management requirements.
- Improve their ability to communicate research results, including building an argument orally and in writing.
- Understand the basic concepts of probability, random variables, statistical inference, hypothesis testing and regression.
- Understand the role of probability in modelling random phenomena that occur in engineering applications.
- Have the ability to analyse experimental and observational data and draw appropriate conclusions.
- Have the ability to apply appropriate statistical analysis to research problems in engineering.
- Have the ability to manipulate data and use Matlab to perform statistical analysis and probability calculations.
最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog Graduate Attribute Course Learning Outcome(s) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
1-7 Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
1-7 Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
1,2 Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
1-7 Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
7 Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
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Learning Resources
Required Resources
None. Notes will be provided.Recommended Resources
Recommended reading:- Handbook of Writing for the Mathematical Sciences, N.J. Higham, SIAM, 1995
- Research Methods for Postgraduates (3e), Tony Greenfield and Sue Greener (editors), Wiley, 2016
- Probability and Statistics for Engineers and Scientists (8e), Jay Devore, Brooks/Cole, 2010
- Statistics in Engineering (2e) - with examples in MATLAB and R, Andrew Metcalfe et al, Chapman & Hall, 2019
Online Learning
The course notes will be available online.
All assignments, tutorials, handouts and solutions where appropriate will also be available online as the course progresses.
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Learning & Teaching Activities
Learning & Teaching Modes
This course relies on instructional videos and workshops as the primary delivery mechanism for the material. It is expected that students will watch the intructional videos, and preread any online notes, to enable them to more actively engage the material and interact during workshops.
Practicals and tutorials supplement the instructional videos and workshops by providing exercises and example problems to enhance student understanding. A sequence of written assignments provides assessment opportunities for students to gauge their progress and understanding.
Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Hours Lectures 12 60 Practicals 6 24 Tutorials 6 24 Assignments 6 48 Total 156 Learning Activities Summary
Lectures
Week 1: Introduction, outline. Introduction to Matlab.
Week 2: Writing and communication. Communication skills in the context of research projects. Requirements. Processes to get you writing. Anatomy of a thesis. The literature review. Informative scientific writing. Citing correctly and avoiding plagiarism.
Week 3: Reponsibilities and ethics: How is a research project different from other parts of your education? The importance of teamwork. Working effectively with your supervisor. Requirements for students. Ethics in science and engineering.
Week 4: Data. Meshing statistics with your research. Reproducibility and transferability of research. Data management. Data formats. Data retention and backups. Revision control.
Week 5: Overview of statistics, sample mean, sample variance and standard deviation. Types of data. Histogram, box plots, five number summary, scatter plots. Sampling.
Week 6: Basic probability theory: axioms of probability, probability rules, conditional probability. Law of total probability, Bayes' theorem, independent events. Permutations and combinations.
Week 7: Discrete random variables: Probability Mass Function (PMF). Bernoulli, binomial, geometric, poisson distributions.
Week 8: Continuous random variables: Probability density function (PDF) and cumulative distribution function (CDF). Uniform, normal, log normal and exponential distributions. Independent random variables, covariance, correlation.
Week 9: Linear combinations of random variables. Distribution of the sample mean, central limit theorem. t-test.
Week 10: Hypothesis testing: test statistic, confidence intervals, significance, P-values, sample size.
Week 11: Linear regression: least squares estimation, inference, prediction, model checking.
Week 12: Multiple linear regression.
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Assessment
The 最新糖心Vlog's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Component Weighting Learning Outcomes Assessed Assignments 18 Formative Research Methods Tutorials 2 Formative Research Methods Practicals 3 Formative Research Methods and Statistics Assignments 18 Formative Statistics Online Tests 9 Summative Research Methods Examination 50 Summative Statistics only Assessment Related Requirements
An aggregate score of at least 50% is required to pass the course.Assessment Detail
Assessment will be based on 5 written or online assignments, active participation in tutorials and practicals, online tests, and a final exam.Submission
All written assignments are to be submitted online. Late assignments will not be accepted.
Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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Student Feedback
The 最新糖心Vlog places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the 最新糖心Vlog to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
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- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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