CORPFIN 7033 - Quantitative Methods (M)

North Terrace Campus - Semester 1 - 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.

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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
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: george.mihaylov@adelaide.edu.au (preferred contact)

George is the lecturer in charge of Quantitative Methods (M) at the 成人大片 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 .

Melbourne Campus students
芒聴戮Students in this course are expected to attend two 1-hour lectures and one 2-hour practical (tutorial) class each week.
芒聴戮PRACTICALS (tutorials) commence in WEEK 2 and ASSESSMENT in practicals BEGINS in WEEK 2.
芒聴戮Melbourne Campus students are asked to refer to MyUni 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)
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

Textbook
David P. Doane & Lori E. Seward, Applied Statistics in Business and Economics, 6th 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 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

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)	5pm Friday, Week 6	20%	1 and 2
Online Test 2 (1 hour)	5pm Friday, 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 hour-long online tests in weeks 6 and 12 will only be available from 3pm to 5pm local Adelaide time (GMT +10:30) on the Friday of each respective week. This is a strict requirement and it is expected that all students plan to ensure their availability to take both of these (compulsory) 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 .

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鈥檚 disciplinary procedures.

The 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 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

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