MATHS 7025 - Research Methods and Statistics
North Terrace Campus - Semester 2 - 2019
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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 Assessment Examination, Statistics assignment, Research Methods assignment Course Staff
Course Coordinator: Andrew Metcalfe
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) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1-7 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1-7 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
1,2 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1-7 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
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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
- Statistics text, tba.
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 lectures as the primary delivery mechanism for the material. The lecturers will guide the students through the material presented in this course in a total of 24 lectures.
It is expected that students will preread the online notes to enable them to more actively engage the material and interact during lectures.
Practicals and tutorials supplement the lectures by providing exercises and example problems to enhance the understanding obtained through lectures. 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 24 60 Practicals 6 24 Tutorials 6 24 Assignments 6 48 Total 156 Learning Activities Summary
Lectures- Introduction, outline, purpose.
- Responsibilities and expectations of research students. How is a research project different from other parts of your education? The importance of teamwork. Working effectively withyour supervisor.
- Research processes. Meshing statistics (e.g. experimental design) with your research. Don’t do the statistics last! Reproducibility and transferability of research.
- Ethics and responsible research. What is ethics in science? What are the requirements for students?
- Communication skills in the context of research projects. Requirements. Processes to get you writing.
- Anatomy of a thesis. Compelling introductions. The literature review. Informative scientific writing.
- Data management. Data cleaning. Data storage.
- Backups and revision control. Requirements and methods for data retention. Revision control and Git.
- Overview of statistics, histogram, sample mean, sample variance and standard deviation.
- Five number summary, scatter plots, sample covariance, sample correlation.
- Basic probability theory 1: axioms of probability, probability rules, conditional probability.
- Basic probability theory 2: law of total probability, Bayes' theorem, independent events.
- Discrete random variables 1: Bernoulli, binomial, geometric distributions.
- Discrete random variables 2: Poisson process, Poisson distribution, mean, variance and standard deviation.
- Continuous random variables: uniform, normal, log normal and exponential distributions.
- Bivariate distributions: independent random variables, covariance, correlation.
- Linear combinations of random variables.
- Random samples, statistics, distribution of the sample mean, central limit theorem.
- Statistical estimation, point estimates, standard errors, confidence intervals.
- Tests of statistical hypotheses, significance, P-values.
- One sample, two independent samples and paired t-tests, inference for proportions.
- Sample size calculation, margin of error, statistical power.
- Linear regression, least squares estimation, inference, prediction, model checking.
- 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 20 Summative and Formative Statistics Assignments 30 Summative and Formative 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 6 written assignments and a final exam.Submission
All written assignments are to be submitted to the designated hand-in boxes within the School of Mathematical Sciences with a signed cover sheet attached. 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
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- 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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