|Year of offer||2017|
|Subject level||Undergraduate Level 2|
|Fees||Subject EFTSL, Level, Discipline & Census Date|
This subject provides students with background mathematical and statistical skills necessary for solving a wide range of commerce problems. It draws heavily on examples from accounting, management and marketing and, to a lesser extent, economics and finance. Topics include: review of statistics; tests of the location of populations; simple and multiple regression for use with time series and cross section data, including interpretation of estimates, hypothesis testing and forecasting, an introduction to diagnostics; Logit models; an introduction to time series methods; and seasonality.
Intended learning outcomes
- Conduct and interpret a number of parametric and non-parametric tests of the location of quantitative populations.
- Complete simple and multiple regression analysis, appropriate tests on regression coefficients, analyse and interpret the results and explain the findings.
- Identify the circumstances under which test procedures may not be valid.
- Analyse several specific models often employed in the various fields within commerce.
- Identify the circumstances under which a model with a binary dependent variable is appropriate.
- Evaluate the results of a Logit model, test relevant hypotheses on the regression coefficients in a Logit model and explain the findings.
- Explain the difficulties that can arise when studying time series data.
- Interpret season factors and seasonally adjust data.
- Employ several methods to analyse and forecast time series data.
- Use and understand various publicly available statistics, including the many data series available describing the economy and markets.
High level of development: collaborative learning; statistical reasoning; application of theory to practice; interpretation and analysis; synthesis of data and other information; evaluation of data and other information; use of computer software.
Moderate level of development: oral communication; written communication; problem solving; critical thinking; receptiveness to alternative ideas.
Some level of development: team work; accessing data and other information from a range of sources.
Eligibility and requirements
One of the following:
|Code||Name||Teaching period||Credit Points|
|ECON10005||Quantitative Methods 1||
|MAST10010||Data Analysis 1||
|MAST10011||Experimental Design and Data Analysis||
Recommended background knowledge
Please refer to Prerequisites and Corequisites.
Core participation requirements
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Student Equity and Disability Support website: http://www.services.unimelb.edu.au/disability
- Three group assignments where students can choose whether they be in a group of one or two, preparing assignments that involve quantitative data analysis and review, with each assignment not exceeding 14 pages (including graphs, charts and equations), due in weeks 5, 9 and 11, (5% each, total 15%)
- A mid-semester online test on material covered prior to week 6, in week 6, (5%)
- Tutorial participation, including completion of weekly tutorial exercises involving quantitative data analysis and review, throughout semester, (10%)
- A 2-hour end-of-semester final examination covering the whole semester's work, end of semester, (70%)
- To pass this subject students must pass the end of semester examination.
Dates & times
- Summer Term
Principal coordinator Wasana Karunarathne Mode of delivery On Campus — Parkville Contact hours Semester 1 and 2: Two 1-hour lectures and a 1-hour tutorial per week; Summer Semester: Two 2-hour lectures and two 1-hour tutorials per week for six weeks Total time commitment 170 hours Teaching period 3 January 2017 to 17 February 2017 Last self-enrol date 12 January 2017 Census date 13 January 2017 Last date to withdraw without fail 10 February 2017 Assessment period ends 25 February 2017
Summer Term contact information
- Semester 1
Principal coordinator Gholamreza Hajargasht Mode of delivery On Campus — Parkville Contact hours Semester 1 and 2: Two 1-hour lectures and a 1-hour tutorial per week; Summer Semester: Two 2-hour lectures and two 1-hour tutorials per week for six weeks Total time commitment 170 hours Teaching period 27 February 2017 to 28 May 2017 Last self-enrol date 10 March 2017 Census date 31 March 2017 Last date to withdraw without fail 5 May 2017 Assessment period ends 23 June 2017
Semester 1 contact information
- Semester 2
Principal coordinator Bill Griffiths Mode of delivery On Campus — Parkville Contact hours Semester 1 and 2: Two 1-hour lectures and a 1-hour tutorial per week; Summer Semester: Two 2-hour lectures and two 1-hour tutorials per week for six weeks Total time commitment 170 hours Teaching period 24 July 2017 to 22 October 2017 Last self-enrol date 4 August 2017 Census date 31 August 2017 Last date to withdraw without fail 22 September 2017 Assessment period ends 17 November 2017
Semester 2 contact information
Time commitment details
You will be advised of prescribed texts by your lecturer.
Related breadth tracks
This subject is available as breadth in the following courses: