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Unit outline_

QBUS5001: Quantitative Methods for Business

Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney

This unit highlights the importance of statistical methods and tools for today's managers and analysts and demonstrates how to apply these methods to business problems using real-world data. The quantitative skills that students learn in this unit are useful in all areas of business. Through taking this unit students learn how to model and analyse the relationships within business data; how to identify the appropriate statistical technique in different business environments; how to compute statistics by hand and using special purpose software; how to interpret results in the context of the business problem; and how to forecast using business data. The unit is taught through data-driven examples, exercises and business case studies.

Unit details and rules

Academic unit Business Analytics
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
ECMT5001 or QBUS5002
Assumed knowledge
? 

Students should be capable of reading data in tabulated form and working with Microsoft EXCEL and doing High School level of mathematics

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Artem Prokhorov, artem.prokhorov@sydney.edu.au
Lecturer(s) Andrew Pratley, andrew.pratley@sydney.edu.au
Nurul Alam, nurul.alam@sydney.edu.au
Tutor(s) Rebecca Chan, rebecca.chan@sydney.edu.au
Type Description Weight Due Length
Small continuous assessment Assignments
n/a
15% - n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Final exam (Record+) Type B final exam hurdle task Final exam
n/a
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Small continuous assessment MindTab Quizzes
n/a
10% Multiple weeks n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
In-semester test (Record+) Type B in-semester exam Mid-semester exam
n/a
25% Week 06
Due date: 28 Sep 2020 at 12:00
50 minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
hurdle task = hurdle task ?
Type B final exam = Type B final exam ?
Type B in-semester exam = Type B in-semester exam ?

Assessment summary

  • MindTab Problem Sets (10%): There will be about 17 online problem sets available via Cengage, the publisher of the textbook for this unit. They will be bundled into about 12 weekly homeworks. Best 10 will be counted towards this part of the mark. Unless otherwise stated, they will be due each Monday. The problem sets will be marked and graded automatically by Cengage. Sometimes you may be allowed to attempt a problem set for up to three times, and in the case your mark will be the average of all attempts.
  • Assignments (15%): These will be about 10 lab exercises that will be posted online and will need to be completed during your allocated tutorial. 
  • Mid-semester exam (25%): The topics covered will be announced on Canvas. This will likely be an online proctored exam.
  • Final exam (50%): The topics covered will be announced on Canvas. This will likely be an online proctored exam.

 

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 Population, sample, parameter, statistic, confidence level and significance set; types of data; types of charts; central location measures; variability measures; Chebyshev inequality; sampling plans and sampling errors; Lecture and tutorial (4 hr)  
Week 02 Events and probabilities; joint, marginal and conditional; axioms of probability; probability tries; Bayes; discrete and continuous distributions; expectation and variance; bivariate distribution; portfolio diversification Lecture and tutorial (4 hr)  
Week 03 Binomial; Poisson; uniform; exponential; normal Lecture and tutorial (4 hr)  
Week 04 Inference; expectation and variance of sample mean; sampling distribution of the mean and proportion; estimation; point estimation Lecture and tutorial (4 hr)  
Week 05 Interval estimate for the mean and proportion; sample size determination; estimation of difference; matched pairs; two proportions Lecture and tutorial (4 hr)  
Week 06 Mid-Term Exam Lecture and tutorial (4 hr)  
Week 07 Hypothesis testing; null and alternative; type 1 and 2 error; power and size; p-value; testing the mean; testing the proportion Lecture and tutorial (4 hr)  
Week 08 Testing of two population means; testing of equal variance; testing of two population probabilities Lecture and tutorial (4 hr)  
Week 09 Experiment design; one-way anova; multiple comparison tests Lecture and tutorial (4 hr)  
Week 10 Coefficient of correlation; SLR; testing and prediction; residual diagnostics Lecture and tutorial (4 hr)  
Week 11 MLR; testing and prediction; R2; stepwise regression Lecture and tutorial (4 hr)  
Week 12 Multicollinearity; dummies; time series; logit; intro to statistical and machine learning methods Lecture and tutorial (4 hr)  

Attendance and class requirements

There are three parts to the unit:

  1. Pre-recorded lectures
  1. Zoom lecture/workshop on Mondays, 12 to 2
  1. Tutorials (see your schedule).

Pre-recorded lectures are required material. They needs to be listened to before the beginning of each week in order to successfully complete the MindTab questions and tutorials. 

Attendance of Zoom lectures/workshops is not required but is highly recommended because it will asnwer any questions you may have after listening to the pre-recording lecture and taking the MindTab problem set.

Attendance of tutorials is required; tutorials will include assignments which will be marked. 

Pre-work: Before the beginning of each week, the following items will be made available for pre-work: a pre-recorded lecture, a list of assigned reading, a MindTab quiz on that material. Students are required to complete the pre-work before coming to the Zoom lecture/workshop and to the tutorials. 

Zoom lectures/workshops will be recorded and made available.  Tutorials will not be.

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

Selvanathan, E. A., Selvanathan, S and Keller, G. (2017) Business Statistics, Australia and New Zealand 7th Edition. Cengage Learning, Australia

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. build a strong quantitative skill set for business decision making; create statistical models for studying relationship amongst business variables; demonstrate proficiency in the use of statistical software for quantitative modelling
  • LO2. evaluate underlying theories, concepts, assumptions and arguments in business related fields
  • LO3. identify problems within real-world constraints and collect data for decision making; manage, analyse, evaluate and use information efficiently and effectively; demonstrate coherent arguments when recommending solutions
  • LO4. communicate confidently and coherently to a professional standard both orally and in writing
  • LO5. defend data integrity; analyse data and report results professionally and ethically.

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.

NA

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.