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Unit outline_

MMGT6012: Business Tools for Management

Intensive May - June, 2022 [Block mode] - Remote

Quantitative analysis is a key activity in developing successful business strategies in the areas of business management. Successful business strategies are generally based on diverse forms of analysis on information collected from a wide range of sources. This unit provides an introduction to the theory and principles of quantitative analysis of business markets through lectures, computer workshops, and practical assessments requiring the analysis of various types of data. Through classes and assessments designed to specifically teach students how to undertake quantitative research in a practical manner, students are able to conduct their own quantitative analysis of market places.

Unit details and rules

Academic unit Management Education
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Matthew Beck, matthew.beck@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final exam
n/a
30% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Tutorial quiz Quiz 1
Short quiz on application of previous lecture
3% Ongoing
Due date: 04 May 2022 at 09:00

Closing date: 04 May 2022
30 minutes / ~250 words
Outcomes assessed: LO2 LO4 LO5
Tutorial quiz Quiz 2
Short quiz on application of previous lecture
3% Ongoing
Due date: 11 May 2022 at 09:00

Closing date: 11 May 2022
30 minutes / ~250 words
Outcomes assessed: LO2 LO4 LO5
Tutorial quiz Quiz 3
Short quiz on application of previous lecture
3% Ongoing
Due date: 18 May 2022 at 09:00

Closing date: 18 May 2022
30 minutes / ~250 words
Outcomes assessed: LO2 LO4 LO5
Tutorial quiz Quiz 4
Short quiz on application of previous lecture
6% Ongoing
Due date: 01 Jun 2022 at 09:00

Closing date: 01 Jun 2022
30 minutes / ~250 words
Outcomes assessed: LO1 LO2 LO4
Assignment group assignment Group assignment
Written report on business problem using insights from statistical analysis
25% Week 05
Due date: 26 May 2022 at 23:59

Closing date: 26 May 2022
3500 words
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
In-semester test (Take-home short release) Type D in-semester exam Computer exam
Short answer questions
30% Week 07
Due date: 03 Jun 2022 at 18:00
2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
group assignment = group assignment ?
Type D final exam = Type D final exam ?
Type D in-semester exam = Type D in-semester exam ?

Assessment summary

  • Quizzes: In the weeks between classes students will be given short online quizzes to complete. The content of the quiz tests the lecture material taught in the lecture that precedes the quiz.
  • Computer exam: Students will be given a series of questions that test the knowledge of the last modules of the course, linear programming and simulation modelling and will be completed under exam conditions in a computer lab.
  • Group assignment: A hypothetical business problem will be explained to students, with groups being given a dataset which they are to analyse such that recommendations surrounding the business problem can be made. The analysis methods deployed will be those taught in the first series of course modules on basic data analysis and presentation, descriptive statistics, inferential statistics and regression analysis.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Distinction

75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Credit

65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 1. Introduction and basic data analysis; 2. Introduction to Excel and graphing; 3. Hypothesis testing and inferencing; 4. Basic analysis and distributions in Excel Lecture and tutorial (8 hr) LO1 LO4
Week 02 1. Tests of differences and relationships; 2. Using Excel for statistical analysis; 3. Introduction to regression; 4. Introduction to regression in Excel Lecture and tutorial (8 hr) LO2 LO3
Week 03 1. Regression in SPSS; 2. Multiple regression models; 3. Developing and interpreting models; 4. Regression diagnostics; 5. Using Excel for advanced regression Lecture and tutorial (8 hr) LO4 LO5
Week 04 1. Time series analysis and forecasting; 2. Using Excel for time series analysis; 3. Decision analysis and spreadsheet modelling; 4. Spreadsheet modelling workshop Lecture and tutorial (8 hr) LO2 LO3
Week 05 1. Linear programming: theory and Excel application; 2. Simulation: theory and Excel application Lecture and tutorial (8 hr) LO4 LO5

Attendance and class requirements

Lecture recordings: All lectures and seminars are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

There are no required textbooks, but the following text is recommended:

  • Rose, J. and Beck, M. (Compilers), (2007) Basic Quantitative Analysis for Management, Pearson Education Australia (Compiled from Basic Business Statistics and Quantitative Analysis for Management).

Useful supplementary texts are:

  • Anderson, D., D. Sweeney and T. Williams (2004), Contemporary management science with spreadsheets, Thompson Learning.
  • Levin, D.M., D. Stephan, T.C. Krehbeil and M.L: Berenson (2005), Statistics for managers using Microsoft Excel, 4th edn, Pearson Prentice Hall.

There are MANY supplementary resources freely available to students on the Canvas site. Those students not comfortable with quantitative methods should browse the course site before the start of semester and read some of the introductory materials, which have been written for those with no prior quantitative study or experience. 

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. apply statistical methods to solve quantitative business problems
  • LO2. apply the logical processes of quantitative analysis to deconstruct complex business problems
  • LO3. evaluate application statistical methods in solving business problems
  • LO4. communicate the results of statistical analysis clearly, concisely and with impact
  • LO5. identify limitations of analysis.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered.

There are extensive online resources available on the Canvas site for the unit, including over 25 tutorial videos, and 2 freely supplied supplementary textbooks. Please make use of all additional resources.

More information can be found on Canvas.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.