Quantitative Decision Making 2

Subject 316-893 (2009)

Note: This is an archived Handbook entry from 2009. Search for this in the current handbook

Credit Points: 12.50
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2009:

For information about these dates, click here.
Time Commitment: Contact Hours: Two 1-hour lectures and one 1-hour workshop per week (Semester 2).
Total Time Commitment: Not available
Prerequisites: 316-661 Quantitative Methods for Business
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
Subject Overview: This subject illustrates the use of quantitative methods to aid managerial decision making. Topics include review of statistics; F and Chi-squared distributions; review of simple linear regression multiple linear regression model; hypothesis testing, forecasting, diagnostics with regression (including heteroskedasticity, serial correlation and model specification).
Objectives:

On successful completion of this subject students should be able to:

  • Apply the least-squares method of estimation to the context of the simple linear regression model;
  • Apply the principles of the least-squares method of estimation and inference to the multiple linear regression model;
  • Apply EViews to estimate, test hypotheses and forecast in the context of the linear regression model; and
  • Explain various problems that arise from applying the linear regression model to data, including multicollinearity, specification errors, heteroskedasticity, nonstationarity and autocorrelation.
Assessment: One 2-hour end-of-semester examination (70%), assignments not exceeding 1500 words in the first half of the semester (15%) and assignments not exceeding 1500 words in the second half of the semester (15%).
Prescribed Texts: None
Breadth Options:

This subject is not available as a breadth subject.
Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

On successful completion of this subject, students should have improved the following generic skills:

  • High level of development: statistical reasoning; application of theory to practice; interpretation and analysis; synthesis of data and other information; evaluation of data and other information; and use of computer software.
  • Moderate level of development: written communication; critical thinking; problem solving; and receptiveness to alternative ideas; evaluation of ideas, views and evidence, synthesis of ideas, views and evidence.
  • Some level of development: collaborative learning and team work; and accessing data and other information; from a range of sources; oral communication; strategic thinking.
Related Course(s): Master of Management (Economics)

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