ACG512 Data Analysis for Financial Decision-making (8)

This subject focuses on the application of quantitative management tools to data analysis in order to assist management decision makers within organisations. It introduces relevant statistical, research and operations management techniques which can be used to enhance the ability of managers to make informed decisions. Use is made of computer software designed to support the analysis, reporting and decision making functions.

No offerings have been identified for this subject in 2020.

Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject Information

Grading System

HD/FL

Duration

One session

School

School of Accounting and Finance

Enrolment Restrictions

Postgraduate students only and not available to students who have completed ACC544

Prerequisites

ACG511

Incompatible Subjects

ACC544

Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to demonstrate problem-solving skills in assessing, organising, summarising and interpreting relevant data for decision making purposes;
  • be able to apply decision theory to business situations;
  • be able to explain and use of simulation and probability concepts for complex decisions;
  • be able to use appropriate computer software designed to support the analysis, reporting and decision making process;
  • be able to demonstrate understanding of the application of statistical hypothesis testing to decisions, with particular emphasis on quality control and interpreting the significance of regression coefficients in cost estimation;
  • be able to use accepted time-series forecasting methods;
  • be able to apply cost-volume-profit analysis and linear programming to product mix decisions, and
  • be able to communicate the interpretation of findings to a diverse audience.

Syllabus

This subject will cover the following topics:
  • Decision theory
  • Simulation modelling
  • Probability concepts
  • Statistical hypothesis testing
  • Regression coefficients
  • Time-series forecasting methods
  • Cost- volume-profit analysis
  • Linear programming
  • Product mix decisions

The information contained in the CSU Handbook was accurate at the date of publication: October 2020. The University reserves the right to vary the information at any time without notice.

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