CORPFIN 7033 - Quantitative Methods (M)

North Terrace Campus - Semester 2 - 2021

The purpose of this course is to provide an introduction to both basic and advanced analytical tools for business disciplines. Beginning with simple statistical methods, the course builds to more robust analytical techniques such as multivariate linear regression. Emphasis is placed on theoretical understanding of concepts as well as the application of key methodologies used by industry. This course also aims to promote a critical perspective on the use of statistical and econometric information.

  • General Course Information
    Course Details
    Course Code CORPFIN 7033
    Course Quantitative Methods (M)
    Coordinating Unit Finance and Banking
    Term Semester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange Y
    Assumed Knowledge SACE Stage 2 Mathematical Methods or equivalent
    Assessment Exam/assignments/tests/tutorial work as prescribed at first lecture
    Course Staff

    Course Coordinator: Dr George Mihaylov

    Adelaide Semester 1 & 2 and Melbourne Campus Trimester 2 & 3:
    Course Coordinator: Dr George Mihaylov

    Dr George Mihaylov (lecturer in charge)
    Location: Room 12.14, Nexus 10, Pulteney Street
    Telephone: 8313 2056 (work)
    Email: (preferred contact)

    George is the 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, 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, including partnerships with 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.

    Melbourne Campus students: 

    - Students in this course are expected to attend two 1-hour lectures and one 2-hour practical (tutorial) class each week.
    - PRACTICALS (tutorials) commence in WEEK 2 and ASSESSMENT in practicals BEGINS in WEEK 2.
    - Melbourne Campus students are asked to refer to MyUni for applicable timetable and assessment information.
  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course, students will be able to:

    1. Explain probability theory and its relation to general statistics

    2. Explain the importance, techniques and biases of quantitative methods in context

    3. Use estimated models to obtain point and interval predictions as well as forecasts

    4. Construct and interpret various statistical hypothesis tests

    5. Critically evaluate regression analysis (model selection)

    6. Critically interpret statistical and econometric results
    University 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)
    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 - 6
    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
    3 - 6
    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
    3 - 6
    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 - 6
  • Learning Resources
    Required Resources
    David P. Doane & Lori E. Seward, Applied Statistics in Business and Economics, 6th ed, McGraw-Hill Irvin.
    Recommended Resources
    This course requires mathematical computation. Although much of it can be handled manually, access to an appropriate calculator is necessary (for those in Face-to-face mode), or alternatively, any software package for data processing, i.e. Microsoft Excel, STATA (for those in Online mode). If you intend to puchase a calculator, it is recommended that you purchase a graphics calculator.
    Online Learning
    All online learning resources in this course will be made available via the MyUni platform. All lectures will be pre-recorded and available online only, whereas tutorials and workshops will be offered on a blended online/ face-to-face basis depending on your enrolment and availability to attend in-person classes.

    To ensure as much as possible that all students are afforded equal opportunity and fairness, all course assessments will be conducted online only.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Adelaide Campus Students (ONLY)

    This course will offer one 2-hour online lecture per week from week 1 to week 12. In addition to the lectures, a 1-hour tutorial class will be offered from week 2 until week 12 and a 1 hour workshop will be offered from week 7 to week 11.

    Tutorial classes will be held weekly commencing the week beginning (Monday 2 August). Membership of tutorial classes is to be finalised by the end of the second week of semester. Students wishing to swap between tutorial classes after this time are required to present their case to the Course Coordinator, but should be aware that such a request may not be approved. Tutorials are an important component of your learning in this course. The communication skills developed in tutorials by regularly and actively participating in discussions are considered to be most important by the School and are highly regarded by employers and professional bodies.

    Melbourne Campus Students (ONLY)

    Students in this course are expected to attend two 1-hour lectures and one 2-hour tutorial class each week, with tutorials commencing in Week 2. Assessment of tutorials also commences in Week 2. Melbourne Campus students are asked to refer to MyUni for any further applicable timetable and assessment information.


    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
    Lectures 1,2,3,4,5,6
    Tutorials 1,2,3,4,5,6
    Workshops 3,4,5,6

    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
    Specific 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:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Task Due Weighting Learning Outcome
    Online test Week 6 20% 1,2,3
    Online test Week 12 30% 3,4,5,6
    Major project (individual) Week 13 50% 1,2,3,4,5,6
    Total 100%
    For specific due dates please see MyUni.
    Assessment Related Requirements
    To gain a pass for this course, a mark of at least 50% overall must be obtained. There is no hurdle requirement in order to pass.

    Legible hand-writing and the quality of English expression are considered to be integral parts of the assessment process. While marks will not be deducted for poor hand-writing or English expression, it is stongly recommended that you write clearly to avoid the complications which can arise for examiners as a result of illegible responses.
    Assessment Detail
    Will be provided on MyUni
    All assessments are to be submitted electronically via MyUni and/or email.
    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.

    Additionally, students who finish in the range 45-49 and meet the criteria within all University Policies will qualify for an academic RAA (Replacement/ Additional Assessment). The RAA will consist of an interview with the course coordinator and gives you the opportunity to demostrate your understanding of the course material in the context of your own previously submitted assessments. The maximum course grade which can be awarded following an academic RAA is Pass 50.

    Final results for this course will be made available through Access Adelaide.

  • 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 ( 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.

  • Student Support
  • Policies & Guidelines
  • 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.

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