CORPFIN 7033 - Quantitative Methods (M)
North Terrace Campus - Winter - 2020
General Course Information
Course Code CORPFIN 7033 Course Quantitative Methods (M) Coordinating Unit Business School Term Winter 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 Course Description 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.
Course Coordinator: Dr Chia-Feng (Jeffrey) YuCourse Coordinator: Dr George Mihaylov
Dr George Mihaylov (lecturer in charge)
Location: Room 12.14, Nexus 10, Pulteney Street
Telephone: 8313 2056 (work)
Email: firstname.lastname@example.org (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.
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn 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
David P. Doane & Lori E. Seward, Applied Statistics in Business and Economics, 6th ed, McGraw-Hill Irvin.
David P. Doane & Lori E. Seward, Applied Statistics in Business and Economics, 5th ed, McGraw-Hill Irvin.
This course requires mathematical computation. Although much of it can be handled manually, access to an appropriate calculator is necessary. If you intend to purchase a calculator for this course, you will find it very useful to purchase a graphics calculator.
Learning & Teaching Activities
Learning & Teaching ModesThe course will be offered in intensive mode over 3 weeks during the Winter School period.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.Students in this course are expected to attend all seminars throughout the Winter School period. 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 of private study outside of your regular classes.
Learning Activities Summary
Learning Activity Related Learning Outcomes Lectures 1,2,3,4,5,6 Exams 1,2,3,4,5,6
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 Task Due Weighting Learning Outcome Online Mid-term Exam Week 2 30% 1,2,3,4,5,6 Online Final Exam Exam period 70% 1,2,3,4,5,6 Total 100%
Assessment Related RequirementsTo 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.
Legible hand-writing and the quality of English expression are considered to be integral parts of the assessment process. Marks may be deducted in all assessments if markers are unable to read or understand answers because of poor hand-writing or English expression.
Students in this course are not permitted to take a DICTIONARY (English or English-Foreign) into the examination.
The use of calculators in the examination is permitted in this course; however graphics calculators must have their memory wiped by exam invigilators.
Assessment DetailWill be provided on MyUni
No information currently available.
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.
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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