CORPFIN 7033NA - Quantitative Methods (M)

Ngee Ann Academy - Trimester 3 - 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.

  • General Course Information
    Course Details
    Course Code CORPFIN 7033NA
    Course Quantitative Methods (M)
    Coordinating Unit Adelaide Business School
    Term Trimester 3
    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 Planner.

  • Learning Outcomes
    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.

    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.

    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’.
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. 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
    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.
    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 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 ( 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
  • 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.

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