You are viewing this page as an international student.
Change to Domestic

You are a domestic student if you are an Australian citizen, a New Zealand citizen or the holder of an Australian permanent visa.

You are an international student whether you are within or outside Australia and you do not meet the domestic student criteria.

Coronavirus (COVID-19): advice and support for the Bond community. Read more

STAT71-112: Quantitative Methods May 2017 [Standard]

General information

This subject develops mathematical and statistical skills necessary for subsequent quantitative subjects in Actuarial Sciences. The development of the mathematical and statistical foundations includes applications of calculus, probability, discrete and continuous random variables, moment generating functions, sampling distributions, hypothesis testing, application of the central limit theorem to large sample inference and data analytics. The R statistical computing package is used as an integral part of the program.

Details

Academic unit:Bond Business School
Subject code:STAT71-112
Subject title:Quantitative Methods
Subject level:Postgraduate
Semester/Year:May 2017
Credit points:10

Delivery & attendance

Timetable: https://bond.edu.au/timetable
Delivery mode:

Standard

Workload items:
  • Tutorial: x11 (Total hours: 22) - Lab Tutorial 2
  • Computer Lab: x12 (Total hours: 24) - Laboratory
  • Lecture: x12 (Total hours: 24) - Weekly Lecture
Attendance and learning activities: It is strongly recommended that you attend all lectures and lab/tutorial sessions. Both materials discussed in lectures and lab sessions are examinable. Most sessions build on the work on the previous one. Consequently, it is difficult to recover if you miss a session. Attendance in tutorials and labs will be monitored, and could impact your final mark in this subject. You run the risk of missing important material as well as crucial guidelines to work through assignment problems and exams if you do not attend.

Resources

Prescribed resources:
  • Mendenhall, W., Beaver, R. J. and Beaver, B. M Introduction to Probability and Statistics. 14th, Cengage Learning
[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 www.bond.edu.au

Enrolment requirements

Requisites: ?

Nil

Restrictions: ?

Nil

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. Understand different types of data and produce appropriate graphical and numerical descriptive statistics.
  2. Understand and apply probability rules and concepts relating to discrete and continuous random variables.
  3. Understand the concept of expectation, variance and moment generating functions for discrete distributions such as Binomial and Poisson, and continuous distributions such as uniform, exponential and Normal.
  4. Understand the importance of the Central Limit Theorem (CLT) and its uses and applications; judging appropriate conditions for its application; use the CLT to find probabilities associated with a range of values for a sample average and sample size determination.
  5. Perform and interpret a variety of hypothesis tests for decision making.
  6. Develop basic data analytics skills.
  7. Use statistical package R most frequently used by practitioners to analyse data.

Assessment

Assessment details

TypeTask%Timing*Outcomes assessed
Written Report Homework Assignments 20% Ongoing 1, 2, 3, 4, 5, 6, 7.
Paper-based Examination (Open) Mid-semester Examination - Week 7 - Saturday 30% Mid-Semester Examination Period 1, 2, 3, 6, 7.
Paper-based Examination (Open) Final Examination 50% Final Examination Period 1, 2, 3, 4, 5, 6, 7.
  • * 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

Homework assignment questions will be assigned for each topic. There will be 4 homeworks for submission. Homework assignments must be submitted at the beginning of lab session as indicated in the subject outline below. The best three will count towards your homeworks grade. Homework submissions by email will not be entertained and it will result in zero marks. Students may work on their assignment in a group but should write their assignment independently in their own words. If it is not written independently, it will be considered as plagiarism. Late submissions of homework assignments will result in zero marks.

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.

Disability 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.

Subject curriculum

Graphical, Numerical Descriptive Measures and Programing with R

1, 7.

Basic Probability Rules, Bayes Law

2, 6.

Binomial and Poisson Probabilities, Expectation and Variance

2, 3, 6, 7.

Differentiation and partial derivatives, HW1 due during computer lab

6.

Integration and basic differential equations

6.

Probabilities, Expectation and Variance, HW2 due during computer lab

No Lecture and No Lab Session

1, 2, 3, 6, 7.

Applications to Central Limit Theorem

4, 7.

Confidence intervals for Mean and Proportions

4, 7.

Mean and Proportions, HW3 due during computer lab

5, 6, 7.

esting Variances and Non-parameteric Tests

5, 6, 7.

HW4 due (during computer lab time)

1, 2, 3, 4, 5, 6, 7.
Approved on: Mar 21, 2017. Edition: 1.1
Last updated: Jun 2, 2017.