CORPFIN 4033 - Quantitative Methods (H)
North Terrace Campus - Semester 1 - 2025
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General Course Information
Course Details
Course Code CORPFIN 4033 Course Quantitative Methods (H) Coordinating Unit Adelaide Business School Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange N Assessment Exam/ major project / mid-semester test as prescribed at first seminar Course Staff
Course Coordinator: Dr George Mihaylov
Course Coordinator: Dr George Mihaylov
Adelaide campus Masters, UAC, and Honours; Melbourne campus.
Location: Room 12.14, Nexus 10, Pulteney Street
Telephone: 8313 2056 (work)
Email: george.mihaylov@adelaide.edu.au (preferred contact)
George is the senior lecturer in charge of Quantitative Methods (M) at the University of Adelaide Business School. He completed his PhD in 2015 and also holds degrees in Mathematical and Computer Sciences (Statistics) and Finance (Honours). His PhD research considers several topical areas of household finance including shared appreciation mortgages, self-managed superannuation and succession in family firms. His research has been published in Urban Studies, Annals of Tourism Research, Journal of Business Research, Applied Economics, Global Finance Journal, International Journal of Managerial Finance, eJournal of Tax Research, and International Review of Financial Analysis. George also has a broad portfolio of consultancies through the International Centre for Financial Services, where he serves as Deputy Director. These include partnerships with Defence Trailblazer, ANZ, Rural Bank, HomeStart Finance, SuperConcepts, Australian Taxation Office and the SMSF Association. He has also previously taught portfolio theory and management, banking, risk management and statistics.Course Timetable
The full timetable of all activities for this course can be accessed from Course Planner.
The schedule of topics for this course is as follows:
1. Quantitative Methods in Context - statistical objectives, ethics, common pitfalls
2. Data Collection and Summary Statistics – graphical and tabular data presentation, summary statistics, common errors in presentation
3. Probability Theory and Concepts – introduction to marginal, joint and conditional probability theory
4. Probability Distributions – introduction to discrete and continuous probability distributions, standard normal distribution transformation
5. Sampling Distribution and Data Collection through Surveys – sampling error, sample mean distribution, central limit theorem, sampling bias
6. The Concept of Interval Estimation – point estimates, confidence intervals and theory, student’s t distribution
7. Hypothesis Testing and Analysis – hypothesis development, significance and decision making, type 1 and 2 errors, analysis of variance (ANOVA)
8. Simple Regression Analysis – correlation, ordinary least squares, coefficient interpretation, the role of residuals in model development and evaluation, modelling assumptions
9. Multivariate Regression Analysis – model interpretation and evaluation, testing for and correcting heteroscedasticity, residual autocorrelation and multicollinearity, dummy variables
10. Introduction to Time Series Analysis and Forecasting – time series decomposition, qualitative and quantitative forecasting, model development and testing -
Learning Outcomes
Course Learning Outcomes
On successful completion of this course, students will be able to:
1. Apply statistical and probability theory to solve problems in research and other contexts
2. Simulate and analyse the relationship between statistical theory and statistical applications
3. Construct and evaluate confidence intervals and hypothesis tests
4. Generate, critique, and revise various statistical models to test for relationships between variablesUniversity Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University 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 - 4 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.
3 - 4 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.
4 Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
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Learning Resources
Required Resources
Textbook
David P. Doane & Lori E. Seward, Applied Statistics in Business and Economics, 7th ed, McGraw-Hill Irvin.
Recommended Resources
Calculations
This course requires mathematical computation. Although much of it can be handled manually, access to an appropriate calculator is recommended, and the use of calculators during (all) assessments is permitted. If you intend to puchase a calculator, it is recommended that you purchase a graphics calculator.Online Learning
All students enrolled in the course are in F2F mode. The in-person seminars employ a flipped classroom L&T model. This means that the seminars are supplemented by lecture recordings that are made available to students via MyUni (online only). All students are expected to watch the weekly lecture recording prior to attending their corresponding F2F seminar. The pedagogical philosophy (and advantages) behind this L&T approach will be discussed in the first seminar. -
Learning & Teaching Activities
Learning & Teaching Modes
This course offers students a weekly 3-hour F2F seminar. Attendance at all seminars is an important component of student learning in this course and is likely to improve student assessment performance. The communication skills developed in class by regular and active participation in discussions are considered to be most important by the Adelaide Business School and are highly regarded by employers and professional bodies.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
The University expects full-time students (i.e. those taking 12 units per semester) to devote a total of 48 hours per week to their studies. This means that you are expected to commit approximately 9 hours for a three-unit course or 13 hours for a four-unit course, of private study outside of your regular classes.
Students in this course are expected to attend all seminars.Learning Activities Summary
Learning Activity Related Learning Outcomes Seminars 1, 2, 3, 4
The schedule of topics for this course is as follows:
1. Quantitative Methods in Context - statistical objectives, ethics, common pitfalls
2. Data Collection and Summary Statistics – graphical and tabular data presentation, summary statistics, common errors in presentation
3. Probability Theory and Concepts – introduction to marginal, joint and conditional probability theory
4. Probability Distributions – introduction to discrete and continuous probability distributions, standard normal distribution transformation
5. Sampling Distribution and Data Collection through Surveys – sampling error, sample mean distribution, central limit theorem, sampling bias
6. The Concept of Interval Estimation – point estimates, confidence intervals and theory, student’s t distribution
7. Hypothesis Testing and Analysis – hypothesis development, significance and decision making, type 1 and 2 errors, analysis of variance (ANOVA)
8. Simple Regression Analysis – correlation, ordinary least squares, coefficient interpretation, the role of residuals in model development and evaluation, modelling assumptions
9. Multivariate Regression Analysis – model interpretation and evaluation, testing for and correcting heteroscedasticity, residual autocorrelation and multicollinearity, dummy variables
10. Introduction to Time Series Analysis and Forecasting – time series decomposition, qualitative and quantitative forecasting, model development and testingSpecific Course Requirements
Weekly Revision Quizzes
The course provides students with the opportunity to revise the contents for each of the 10 topics on a weekly basis via 5-question formative quizzes. The quizzes are non-graded. They contain fundamental questions relating to basic concepts in each topic and are designed primarily to accomodate student engagement. They are not designed to be difficult and do not reflect the difficulty level of the assessments. -
Assessment
The University'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
Specific due dates and other details will be published on MyUni closer to the date of each assessment.Assessment Task Due Weighting Learning Outcome Mid-semester test Week 6 15% 1, 2 Major project (groupwork) Week 13 15% 1, 3, 4 Final exam Exam week 70% 1, 2, 3, 4 Total 100% Assessment Related Requirements
To gain a pass for this course, a mark of at least 50% overall needs to be obtained. There is no hurdle requirement in order to pass.
The quality of English expression is considered to be an integral part of the assessment process. Marks may be deducted in any assessment because of poor English expression. In particular, it is strongly recommended that you proofread and edit your Major Project submission.Assessment Detail
TBA on MyUni.Submission
TBA via MyUni.Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M11 (Honours Mark Scheme) Grade Grade reflects following criteria for allocation of grade Reported on Official Transcript Fail A mark between 1-49 F Third Class A mark between 50-59 3 Second Class Div B A mark between 60-69 2B Second Class Div A A mark between 70-79 2A First Class A mark between 80-100 1 Result Pending An interim result RP Continuing Continuing CN 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 Access Adelaide.
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Student Feedback
The University 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 University 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
- International Student Support
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
- YouX Student Care - Advocacy, confidential counselling, welfare support and advice
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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
- Elder Conservatorium of Music Noise Management Plan
- 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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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.
The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
