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STAT11-112: Quantitative Methods January 2019 [Standard]

General information

Designed to foster the development of foundational mathematical and statistical skills necessary for subsequent quantitative subjects in the Bond Business School. This includes applications of calculus, probability, discrete and continuous random variables, sampling distributions, hypothesis testing, and application of the central limit theorem to large sample inference and data analytics. The use of popular statistical computing packages are integral to providing an applied approach to these topics.


Academic unit:Bond Business School
Subject code:STAT11-112
Subject title:Quantitative Methods
Subject level:Undergraduate
Semester/Year:January 2019
Credit points:10

Delivery & attendance

Delivery mode:


Workload items:
  • Lecture: x12 (Total hours: 24) - Weekly Lecture
  • Tutorial: x12 (Total hours: 24) - Lab Tutorial
  • Personal Study Hours: x12 (Total hours: 72) - Study time and reviewing materials
Attendance and learning activities: Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible.


Prescribed resources:
  • Mendenhall, W., Beaver, R. J. and Beaver, B. M. Introduction to Probability and Statistics. 14th, Cengage Learning
After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.
[email protected] & Email:[email protected] is the online learning environment at Bond University and is used to provide access to subject materials, lecture recordings and detailed subject information regarding the subject curriculum, assessment and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.

To access these services, log on to the Student Portal from the Bond University website as

Enrolment requirements

Requisites: ?


Restrictions: ?


Assurance of learning

Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.

At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.

Program Learning Outcomes (PLOs)

Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.

Find your program

Subject Learning Outcomes (SLOs)

On successful completion of this subject the learner will be able to:
  1. Recognise different types of data and produce appropriate graphical and numerical descriptive statistics.
  2. Apply probability rules and concepts relating to discrete and continuous random variables to answer questions within a business context.
  3. Apply the concept of expectation and variance for discrete distributions such as Binomial and Poisson, and continuous distributions such as Uniform, Exponential and Normal to answer questions within a business context.
  4. Demonstrate knowledge of the importance of the Central Limit Theorem (CLT) and its uses and applications.
  5. Conduct and interpret a variety of hypothesis tests to aid decision making in a business context.
  6. Use a statistical package frequently used by practitioners to analyse the data using techniques from SLOs 1-5.


Assessment details

TypeTask%Timing*Outcomes assessed
*Homework Multiple Homework Assignments - short answer analytical questions 30% Progressive 1, 2, 3, 4, 5, 6.
Computer-Aided Examination (Open) Comprehensive Final examination - in computer labs. Exam format is a combination of statistical and spreadsheet software applications (e.g., Excel, R) and written answers. 40% Final Examination Period 1, 2, 3, 4, 5, 6.
Computer-Aided Examination (Open) Mid-semester Examination - Week 7 - Saturday Mid-semester examination - in computer labs. Exam format is a combination of statistical and spreadsheet software applications (e.g., Excel, R) and written answers. 30% Week 7 (Mid-Semester Examination Period) 1, 2, 3, 6.
  • * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
  • C = Students must reach a level of competency to successfully complete this assessment.

Assessment criteria

High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking.
Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas.
Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above.
Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment.
Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines.

Quality assurance

For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.

Study information

Submission procedures

Students must check the [email protected] subject site for detailed assessment information and submission procedures.

Policy on late submission and extensions

There will be four homework assignments in total. The best three will count towards your homework assignment mark. Late submissions of homework assignments will not be considered for marks. Two assignments will be due before the mid-semester exam and two will be due after. Details including precise deadlines will be communicated in class and on iLearn. Students may work on their assignments in a group but should write their assignment independently in their own words. If it is not written independently, it will be considered plagiarism.

Policy on plagiarism

University’s Academic Integrity Policy defines plagiarism as the act of misrepresenting as one’s own original work: another’s ideas, interpretations, words, or creative works; and/or one’s own previous ideas, interpretations, words, or creative work without acknowledging that it was used previously (i.e., self-plagiarism). The University considers the act of plagiarising to be a breach of the Student Conduct Code and, therefore, subject to the Discipline Regulations which provide for a range of penalties including the reduction of marks or grades, fines and suspension from the University.

Bond University utilises Originality Reporting software to inform academic integrity.

Feedback on assessment

Feedback on assessment will be provided to students within two weeks of the assessment submission due date, as per the Assessment Policy.

Accessibility and Inclusion Support

If you have a disability, illness, injury or health condition that impacts your capacity to complete studies, exams or assessment tasks, it is important you let us know your special requirements, early in the semester. Students will need to make an application for support and submit it with recent, comprehensive documentation at an appointment with a Disability Officer. Students with a disability are encouraged to contact the Disability Office at the earliest possible time, to meet staff and learn about the services available to meet your specific needs. Please note that late notification or failure to disclose your disability can be to your disadvantage as the University cannot guarantee support under such circumstances.

Additional subject information

As part of the requirements for Business School quality accreditation, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.

Subject curriculum

This topic begins with an introduction to basic statistical concepts and definitions. A variety of graphs are then discussed including pie charts, bar charts, boxplots, histograms, line charts and scatter plots. Basic numerical descriptive statistics are also covered including measures of central location, variability, shape, relative standing and linear association.


This topic introduces the concept of probability and basic probability rules. It also covers composite events and the counting rules used to handle them. The concepts of independence and Bayes’ Rule and discrete random variables and their applications.


This topic covers two major discrete probability distributions: Binomial and Poisson. For each, expectation, variance and a variety of probability calculations within business contexts are covered. These two distributions have many practical applications and are commonly used to model the number of occurrences of events in a fixed number of trials or a fixed amount of time.

2, 3.

All the major differentiation rules are covered, including those for logs and exponentials. Higher-order derivatives, partial derivatives and total differentiation are also covered. Differentiation is then used to perform unconstrained optimisation in a business context such as profit maximisation, cost minimisation and utility maximisation

This topic includes a review of integration and all the basic rules. It also covers definite integrals and integration by parts. The concept of basic differential equations and how to solve them is also introduced with a focus on first-order differential equations.

The difference between discrete probability distributions and continuous probability distributions is first discussed. This topic then covers three major continuous probability distributions that have many practical applications: Uniform, Exponential and Normal. For each, expectation, variance and a variety of probability calculations within business contexts are covered.

1, 4.

After discussing the difference between non-probability and probability sampling, the different types of probability sampling are discussed – these include simple random sampling, systematic sampling, stratified sampling and cluster sampling. Following that, sampling distributions of the Mean and Proportion are presented. This includes an explanation and application of the important statistical theorem known as the Central Limit Theorem.

1, 2, 4.

The differences between point and intervals estimates are first discussed. Confidence intervals for both the Mean and Proportion are then explained. This knowledge is then used to estimate the sample size needed in specific circumstances to inform the data collection process.


The fundamentals of hypothesis testing are presented. This includes the concepts of the null and alternative hypotheses, one-tailed and two-tailed tests, possible test outcomes, possible error types and statistical significance levels. Specific tests for Mean (one and two populations) and Proportions are then explained.

Hypothesis tests for variance, both one and two population are taught. Following that, non-parametric hypothesis tests are introduced with some business examples.

Approved on: Nov 15, 2018. Edition: 2.4