CORPFIN 7033NA - Quantitative Methods (M)

Ngee Ann Academy - Trimester 1 - 2017

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.

Open All

Course Details

Course Code	CORPFIN 7033NA
Course	Quantitative Methods (M)
Coordinating Unit	Adelaide Business School
Term	Trimester 1
Level	Postgraduate Coursework
Location/s	Ngee Ann Academy
Units	3
Contact	Up to 4 hourse per week
Available for Study Abroad and Exchange	Y
Incompatible	COMMERCE 7003NA
Assumed Knowledge	SACE Stage 2 Mathematical Methods or equivalent
Assessment	Exam/assignments/tests/tutorial work as prescribed at first lecture

Course Staff

Course Coordinator: Professor Ralf Zurbrugg

Course Timetable

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

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

A list of recommended texts are provided by NAAEC.

Calculator
This course requires mathematical computation.
Access to at least a basic calculator is essential.
Learning & Teaching Activities

Learning & Teaching Modes

This Course is organised into 9 topics (see below). Two weekends of intensive study sessions are scheduled. The first weekend will cover the first half of the program, namely, roughly Topics 1 to 6; whilst the second weekend will cover the second half, namely, roughly Topics 7-9. Students must read relevant chapters and Topic Notes prior to attending the weekend intensives.

The intensive study weekends will involve for each Topic:
• a review of the major statistical ideas and theorems and their applications
• a discussion relating to some suggested activities
• an analysis of some case study exercises
• a discussion and/or review of possible and/or selected final exam questions

The main learning modes are intensive classes combined with self-study supported by problem-solving oriented assignments which develop and reinforce material covered in intensive classes.

Workload

The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

This course expects the standard workload from students expected for a 3 unit course.

Learning Activities Summary

This Course is divided into the following 9 topics:

1 Introduction to Statistics and Data Collection
2 Probability Theory and Concepts
3 Probability Distributions
4 Sampling Distributions & Data Collection through Surveys
5 Interval Estimation & Confidence Intervals
6 Hypothesis Testing and Analysis
7 Simple Regression Analysis
8 & 9 Multivariate Regression Analysis

Students are expected to (preferably) have a basic knowledge of the use of spreadsheet software (e.g. Excel). No prior knowledge of statistics or econometrics is assumed.

In undertaking this Course all students will find themselves exposed to some new material. However, students from a business or finance background may find that some, perhaps much, of this Course traverses previously covered material. This is unavoidable – given the varying backgrounds of students – but also consistent with the objective of foundation Courses in this degree, namely, to ensure all students have a mastery of the ‘first principles’ in the respective subject area regardless of prior study.

In the case of Quantitative Methods the focus is first on a mastery of some core concepts (sampling distributions, test statistics, parameter estimates, p values etc) and essential theorems (e.g. the central limit theorem & Gauss-Markov theorem) and then on ‘problem solving’.

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	Weighting	Learning Outcome
Statistical report/ Case study (relating to Intensive Session I, i.e. Topics 1-7)	20%	1,2,3,4
Statistical report/ Case study (relating to Intensive Session II, i.e. Topics 8-9)	20%	1,2,3,4,5,6
Final Exam Closed book	60%	1,2,3,4,5,6
Total	100%

For specific due dates please see MyUni.

Assessment Related Requirements

Attendance
Statutory obligations in Singapore are such that attendance in person is a compulsory condition of passing a course. Our specific requirements are that students must attend at least 80% of class sessions to be graded for that course. For these purposes each intensive weekend is defined as comprising 5 sessions with 1 on Friday evening and 2 on each of Saturday and Sunday.

Each course in total comprises 10 sessions; Students must attend a minimum of 8 sessions to be eligible to be given a grade for the course. Students failing to meet these requirements will be automatically graded 0% Fail (F) on their transcripts.

Minimum Achievement
Students must attain, at least, an average value of fifty percent (50%) for all assessed items in order to pass the overall course.

Assessment Detail

To be provided by Assignment Information Sheet at the first intensive class.

Submission

Students must submit both hard and soft copies of all assignments.

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

成人大片