CORPFIN 4033 - Quantitative Methods (H)

North Terrace Campus - Semester 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.

  • General Course Information
    Course Details
    Course Code CORPFIN 4033
    Course Quantitative Methods (H)
    Coordinating Unit Business School
    Term Semester 1
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange N
    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 Staff

    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 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. Other research interests include financial products and services as well as commodities. George has also been active in industry projects through the International Centre for Financial Services, including partnerships with ANZ, Rural Bank, HomeStart Finance and the SMSF Association. George has a broad spectrum of teaching interests. Previously he has taught portfolio theory and management, banking, risk management and statistics.

    Mr John Dolinis (tutor and workshop co-ordinator)

    Email: john.dolinis@adelaide.edu.au (preferred contact) OR dolinis@yahoo.com.au

    John is a tutor and workshop co-ordinator for Quantitative Methods (M). He holds a Bachelor degree in Applied Mathematics and Physics; a Master degree in Public Health (epidemiology and biostatistics); and a Master degree in Accounting and Finance. He is currently teaching accounting and quantitative methods subjects at other tertiary education institutions in Adelaide. He has prior health and medical research experience, particularly in the field of injury analysis, surveillance and prevention.
    Course Timetable

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

    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
  • 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
    Textbook (any one of the following)

    David P. Doane & Lori E. Seward, Applied Statistics in Business and Economics, 5th ed, McGraw-Hill Irvin.

    David P. Doane & Lori E. Seward, Applied Statistics in Business and Economics, 4th ed, McGraw-Hill Irvin.

    David P. Doane & Lori E. Seward, Applied Statistics in Business and Economics, 3rd ed, McGraw-Hill Irvin.

    Recommended Resources
    Calculator

    This course requires mathematical computation. Although much of it is relatively simple, 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 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 the week beginning (Monday 7 March). 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 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
    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
  • 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 Due Date and Time (& Location if applicable) Weighting Related Learning Outcome
    Mid-term Exam Friday 8th of April at 3pm, Bonython Hall 15% 1 - 6
    Major Project Week 12, Time and Date TBA 15% 1 - 8
    Final Exam See Access Adelaide 70% 1 - 8
    Total 100%
    Assessment Related Requirements
    To gain a pass for this course, a mark of at least 50% overall needs to be obtained.

    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 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.

    Students are also permitted to bring into the examination, 1 double sided A4 page of hand-written notes. If this note page contains computer writing, it will be confiscated and students may be reported for academic misconduct.
    Assessment Detail
    Will be provided on MyUni
    Submission
    The 1 hour mid-term exam will be held in Bonython Hall during the weekly lecture time in week 6 – test papers are to be submitted immediately following this time. Major project submissions are to be made through the postgraduate hub at Nexus 10 Pulteney Street.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M11 (Honours Mark Scheme)
    GradeGrade reflects following criteria for allocation of gradeReported on Official Transcript
    Fail A mark between 1-49 F
    Third Class A mark between 50-59 3
    Second Class Div B A mark between 60-69 2B
    Second Class Div A A mark between 70-79 2A
    First Class A mark between 80-100 1
    Result Pending An interim result RP
    Continuing Continuing CN

    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 (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
  • 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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