CORPFIN 7033 - Quantitative Methods (M)
North Terrace Campus - Semester 2 - 2024
General Course Information
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 Incompatible COMMERCE 7003NA, CORPFIN 7033MELB 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 George MihaylovCourse 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: email@example.com (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, 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 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.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 OutcomesOn successful completion of this course, students will be able to:
1. Apply statistical and probability theory to solve problems in context
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 regression models to test for complex relationships between variables
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 - 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.
1 - 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.
Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
David P. Doane & Lori E. Seward, Applied Statistics in Business and Economics, 7th ed, McGraw-Hill Irvin.
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 LearningAll 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 ModesThis 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.
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 testing
Specific Course RequirementsWeekly 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.
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 Mid-semester test (online) 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%
IMPORTANT: The online mid-semester test in week 6 will only be available for a set (limited) period of time. As a strict requirement, it is expected that all students plan to ensure their availability to take this (compulsory) graded assessment.
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.
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 DetailTBA via MyUni.
SubmissionTBA via MyUni.
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.
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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