CORPFIN 7033 - Quantitative Methods (M)

North Terrace Campus - Semester 1 - 2022

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.

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Course Details

Course Code	CORPFIN 7033
Course	Quantitative Methods (M)
Coordinating Unit	Finance and Banking
Term	Semester 1
Level	Postgraduate Coursework
Location/s	North Terrace Campus
Units	3
Contact	Up to 4 hours per week for Semester offerings, Summer School intensive up to 8 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

Course Coordinator: Dr George Mihaylov
Adelaide campus Masters, UAC, and Honours; Melbourne campus.

Dr George Mihaylov (lecturer in charge)
Location: Room 12.14, Nexus 10, Pulteney Street
Telephone: 8313 2056 (work)
Email: george.mihaylov@adelaide.edu.au (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.

Adelaide Semester 1		Melbourne Semester 1
Name:	Dr George Mihaylov	Name:	Akwasi Ampofo
Email:	george.mihaylov@adelaide.edu.au	Email:	akwasi.ampofo@adelaide.edu.au
Adelaide Semester 2		Melbourne Semester 2
Name:	Dr George Mihaylov	Name:	To Be Confirmed
Email:	george.mihaylov@adelaide.edu.au	Email:

Course Timetable

The full timetable of all activities for this course can be accessed from Course Planner.

Melbourne Campus students (only)
Students sitting the Melbourne campus version of this course are expected to attend two 1-hour lectures and one 2-hour tutorial class each week.
Tutorials commence in Week 2 and are assessed also beginning in Week 2.
Melbourne campus students are asked to refer to their MyUni page for applicable timetable and assessment information.

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)
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 - 6
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 - 6
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.	3 - 6
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 - 6

Learning Resources

Required Resources

Textbook

David P. Doane & Lori E. Seward, Applied Statistics in Business and Economics, 6th ed, McGraw-Hill Irvin.

OR

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 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, 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. This means that there are F2F class options for all students located in Adelaide who wish to attend, but also that there are online equivalents (recordings or live streamed classes over Zoom) for each course component.

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

This course will offer one 2-hour 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.

TUTORIALS:

Tutorial classes will be held weekly commencing in Week 2. 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 Lecturer in Charge, 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 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

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

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

Assessment Task	Due	Weighting	Learning Outcomes
Online Test 1 (1 hour)	4pm Tuesday, Week 6	20%	1 and 2
Online Test 2 (1.5 hours)	4pm Tuesday, Week 12	30%	3, 4, 5 and 6
Major Project (Individual)	Week 13	50%	1, 2, 3, 4, 5 and 6
Total		100%

For specific due dates please see MyUni.

IMPORTANT: The online tests in weeks 6 and 12 will only be available between 2pm and 4pm local Adelaide time (GMT +10:30) on the Tuesday of each respective week (during our regular lecture time). As a strict requirement, it is expected that all students plan to ensure their availability to take both of these (compulsory) graded tests.

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.

All available resources are permitted to be used during all assessments. This includes the course materials and textbook, all software packages, and the internet. Please note however, all assessments (and both online tests in particular) contain multiple anti-plagiarism/ cheating/ collusion measures. These include, but are not limited to, randomised question banks, randomised answer responses, strict timing and availability of assessments, and submission processing through Turnitin.

Assessment Detail

Will be provided on MyUni

Submission

All assessments are to be submitted online via MyUni.

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 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 (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.
Student Support
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
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.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator