Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location in first half year 2021.
About this subject
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The 2021 timetable will be available on 8 December, and after this date you will be able to view the classes for all 2021 subjects. Timetable preference entry will open for Summer subjects on 8 December. Visit the class timetable page for more information on creating your timetable.
Please refer to the specific study period for contact information.
Summer Term - Online
Semester 1 - Dual-Delivery
Semester 2 - Dual-Delivery
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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; autoregressive distributed lag models and testing for stationarity.
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.
- Analyse autoregressive distributed lag models and testing for stationarity.
- 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.
Last updated: 28 November 2020