Students
ACST602 – Statistical Modelling in Finance and Insurance
2014 – S2 Day
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Lecturer
Nino Kordzakhia
E4A 537
Refer to iLearn
Lecturer
Suzanne Curtis
E4A 552
Refer to iLearn
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|---|---|
| Credit points |
Credit points
4
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| Prerequisites |
Prerequisites
ACST601 and ACST604
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| Corequisites |
Corequisites
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| Co-badged status |
Co-badged status
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| Unit description |
Unit description
This unit covers linear statistical modelling in insurance and finance. Topics include: simple and multiple linear regression; ANOVA models; analysis of residuals, regression diagnostics and influential observations; theory of estimation; method of moments and maximum likelihood; properties of estimators; sampling distributions and properties of sample statistics; the t, F and X2 distributions; confidence intervals and hypothesis testing in a regression context; type I and II errors; power; chi squared tests; criteria for choosing models; goodness of fit tests, tests of association and homogeneity; and applications of linear modelling to problems in insurance and finance.
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Important Academic Dates
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
Learning Outcomes
On successful completion of this unit, you will be able to:
- Understand the theory of estimation and sampling distribution;
- Have a solid understanding of hypothesis test and linear regression models;
- Understand and be able to carry out one and two sample tests, and chi-square tests;
- Be able to assess model fit for simple regression models and have a solid understanding of model diagnostics;
- Be able to interpret results from hypothesis tests and linear regression models;
- Use R statistical package to carry out various hypothesis tests, fit simple linear regression models with continuous or categorical covariates, and produce relevant statistical plots/graphs.
Assessment Tasks
| Name | Weighting | Due |
|---|---|---|
| Assessed Coursework | 10% | Weekly |
| Class test | 20% | 10.05 am Week 7 |
| Assignment | 10% | 11.05 am Week 11 |
| Final examination | 60% | University exam timetable |
Assessed Coursework
Due: Weekly
Weighting: 10%
One-hour tutorials will start on Thursday, Week 2.
In weeks 2 to 12 you will be required to submit tutorial and homework.
Students need to submit a hard copy of their solutions for all tutorial and homework questions at the beginning of tutorial class. Tutorial and homework questions are equally weighted and together worth 10% of the unit assessment.
No extensions will be granted. Students who have not submitted the solution to tutorial and homework questions will be awarded a mark of 0 for the task, except for cases in which an application for special consideration is made and approved.
On successful completion you will be able to:
- Understand the theory of estimation and sampling distribution;
- Have a solid understanding of hypothesis test and linear regression models;
- Understand and be able to carry out one and two sample tests, and chi-square tests;
- Be able to assess model fit for simple regression models and have a solid understanding of model diagnostics;
- Be able to interpret results from hypothesis tests and linear regression models;
- Use R statistical package to carry out various hypothesis tests, fit simple linear regression models with continuous or categorical covariates, and produce relevant statistical plots/graphs.
Class test
Due: 10.05 am Week 7
Weighting: 20%
The Class Test will be held in the lecture and covers the first 5 weeks of the material.
The Class Test will commence at 10.05 am, Thursday, Week 7.
The class test will be 50 minutes long.
You are permitted ONE A4 page of paper containing reference material printed on both sides. The material may be handwritten.
On successful completion you will be able to:
- Understand the theory of estimation and sampling distribution;
Assignment
Due: 11.05 am Week 11
Weighting: 10%
Assignment questions will be made available through iLearn.
Assignment is to be submitted in the class at 11.05 am, Thursday, Week 11.
No extensions will be granted. Students who have not submitted Assignment on time will be awarded a mark of 0 for the task, except for cases in which an application for special consideration is made and approved.
On successful completion you will be able to:
- Understand the theory of estimation and sampling distribution;
- Have a solid understanding of hypothesis test and linear regression models;
- Understand and be able to carry out one and two sample tests, and chi-square tests;
- Be able to assess model fit for simple regression models and have a solid understanding of model diagnostics;
- Be able to interpret results from hypothesis tests and linear regression models;
- Use R statistical package to carry out various hypothesis tests, fit simple linear regression models with continuous or categorical covariates, and produce relevant statistical plots/graphs.
Final examination
Due: University exam timetable
Weighting: 60%
A three-hour final examination for this unit will be held during the University Examination period.
You are permitted TWO A4 pages of paper containing reference material printed on both sides. The material may be handwritten. The pages will not be returned at the end of the final examination.
Calculators will be needed but must not be of the text/programmable type.
To be eligible to pass this unit, a pass is required in the final examination.
You are expected to present yourself for examination at the time and place designated in the University Examination Timetable. The timetable will be available in Draft form approximately eight weeks before the commencement of the examinations and in Final form approximately four weeks before the commencement of the examinations.
The Macquarie university examination policy details can be viewed at
On successful completion you will be able to:
- Understand the theory of estimation and sampling distribution;
- Have a solid understanding of hypothesis test and linear regression models;
- Understand and be able to carry out one and two sample tests, and chi-square tests;
- Be able to assess model fit for simple regression models and have a solid understanding of model diagnostics;
- Be able to interpret results from hypothesis tests and linear regression models;
- Use R statistical package to carry out various hypothesis tests, fit simple linear regression models with continuous or categorical covariates, and produce relevant statistical plots/graphs.
Delivery and Resources
Technology required
The statistical software R will be used. This is a free software environment for statistical computing and graphics and is downloadable from the website
in versions for Windows, MacOS and Unix platforms. R is also available in the computer labs in E4B. It is convenient to bring a memory stick when using these computers.
Lab opening hours and conditions of use can be found at
WARNING: students are strongly advised not to remain alone in the labs after normal office hours. You should seek out a lab that has other students working in it and/or has a lab monitor.
You are encouraged to phone 9850 7112 (ext. 7112 from inside the lab) at any time after hours, during term time, if you require an escort to your vehicle or public transport.
Classes
Students will attend three one-hour lectures and one one-hour tutorial per week. The lecture notes will be available on iLearn before the lecture. Tutorial and homework exercises will be set weekly and will be available on iLearn before the tutorial.
The timetable for classes can be found at: http://www.timetables.mq.edu.au
iLearn
All unit materials, including administrative updates, lecture notes, tutorials and assignments, will be posted on the Unit website on iLearn at
https://ilearn.mq.edu.au/login/MQ/
Required and recommended texts and materials
“Mathematical Statistics with Applications” W Mendenhall, D Wackerly and R Scheaffer (library call number is QA276.M426) is the recommended textbook for this unit.
References that may be useful:
Chatterjee, S. Hadi, A. and Price, B. (2006). Regres s ion Analys is by Example, John Wiley and Sons , QA278.2.C5
Devore, J. L. (1995). Probability and Statis tics for Engineering and the Sciences , Duxbury Pres s , QA273.D46
Frees , E. W. (2010). Regres s ion Modeling with Actuarial and Financial applications , Cambridge, HG8 7 8 1.F6 7
Kleinbaum D., Kupper, L.L., et al (1998). Applied Regres s ion Analys is and Other Multivariable Methods , (3rd
Edition) Brooks /Cole, QA278.A665
Faraway, J.J. (2002). Practical Regression and ANOVA us ing R. R. http://cran.r-project.org/doc/contrib/Faraway-PRA.pdf
R Development Core Team: An Introduction to R. http://cran.r-project.org/doc/manuals /R-intro.pdf
"The R G uide" (vers ion 2.5) by Jas on Owen. http://cran.r-project.org/doc/contrib/Owen-TheRGuide.pdf
Copies of these books are held in the Reserve section of the library.
Unit Schedule
|
Date |
Week |
Topic |
Assessment |
|
4 August |
1 |
Functions of Random Variables Sampling distribution and the CLT |
Tutorial 1 handed out
|
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11 August |
2 |
Estimation |
Tutorial 1 in Tutorial 2 out |
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18 August |
3 |
Estimation (cont.) |
Tutorial 2 in Tutorial 3 out
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25 August |
4 |
Methods of Estimation |
Tutorial 3 in Tutorial 4 out
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1 September |
5 |
Methodology of statistical tests. Test of population mean |
Tutorial 4 in Tutorial 5 out
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8 September |
6 |
Type I and II errors. Power of tests and sample size |
Tutorial 5 in Tutorial 6 out |
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15 September |
7 |
Paired and two sample problems |
Tutorial 6 in Tutorial 7 out CLASS TEST |
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22 September – 6 October
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Mid-session break 6/10 Labour Day – NSW Public Holiday
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7 October |
8 |
Linear Models and Estimation by Least Square |
Tutorial 7 in Tutorial 8 out
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13 October |
9 |
Linear Models and Estimation by Least Square (cont.) |
Tutorial 8 in Tutorial 9 out
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20 October |
10 |
The Analysis of Variance |
Tutorial 9 in Tutorial 10 out
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27 October |
11 |
Nonparametric Statistics |
Tutorial 10 in Tutorial 11out Assignment is due |
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3 November |
12 |
Nonparametric Statistics (cont.) |
Tutorial 11 in
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10 November |
13 |
REVISION |
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Policies and Procedures
Macquarie University policies and procedures are accessible from Policy Central. Students should be aware of the following policies in particular with regard to Learning and Teaching:
Academic Honesty Policy http://mq.edu.au/policy/docs/academic_honesty/policy.html
Assessment Policy http://mq.edu.au/policy/docs/assessment/policy.html
Grading Policy http://mq.edu.au/policy/docs/grading/policy.html
Grade Appeal Policy http://mq.edu.au/policy/docs/gradeappeal/policy.html
Grievance Management Policy http://mq.edu.au/policy/docs/grievance_management/policy.html
Disruption to Studies Policy http://www.mq.edu.au/policy/docs/disruption_studies/policy.html The Disruption to Studies Policy is effective from March 3 2014 and replaces the Special Consideration Policy.
In addition, a number of other policies can be found in the Learning and Teaching Category of Policy Central.
Student Code of Conduct
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/support/student_conduct/
Supplementary Exams
Further information regarding supplementary exams, including dates, is available here
Student Support
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Learning Skills
Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.
Student Services and Support
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
Student Enquiries
For all student enquiries, visit Student Connect at ask.mq.edu.au
IT Help
For help with University computer systems and technology, visit http://informatics.mq.edu.au/help/.
When using the University's IT, you must adhere to the Acceptable Use Policy. The policy applies to all who connect to the MQ network including students.
Graduate Capabilities
PG - Discipline Knowledge and Skills
Our postgraduates will be able to demonstrate a significantly enhanced depth and breadth of knowledge, scholarly understanding, and specific subject content knowledge in their chosen fields.
This graduate capability is supported by:
Learning outcomes
- Understand the theory of estimation and sampling distribution;
- Have a solid understanding of hypothesis test and linear regression models;
- Understand and be able to carry out one and two sample tests, and chi-square tests;
- Be able to assess model fit for simple regression models and have a solid understanding of model diagnostics;
- Be able to interpret results from hypothesis tests and linear regression models;
- Use R statistical package to carry out various hypothesis tests, fit simple linear regression models with continuous or categorical covariates, and produce relevant statistical plots/graphs.
Assessment tasks
- Assessed Coursework
- Class test
- Assignment
- Final examination
PG - Critical, Analytical and Integrative Thinking
Our postgraduates will be capable of utilising and reflecting on prior knowledge and experience, of applying higher level critical thinking skills, and of integrating and synthesising learning and knowledge from a range of sources and environments. A characteristic of this form of thinking is the generation of new, professionally oriented knowledge through personal or group-based critique of practice and theory.
This graduate capability is supported by:
Learning outcomes
- Understand the theory of estimation and sampling distribution;
- Have a solid understanding of hypothesis test and linear regression models;
- Understand and be able to carry out one and two sample tests, and chi-square tests;
- Be able to assess model fit for simple regression models and have a solid understanding of model diagnostics;
- Be able to interpret results from hypothesis tests and linear regression models;
- Use R statistical package to carry out various hypothesis tests, fit simple linear regression models with continuous or categorical covariates, and produce relevant statistical plots/graphs.
Assessment tasks
- Assessed Coursework
- Class test
- Assignment
- Final examination
PG - Research and Problem Solving Capability
Our postgraduates will be capable of systematic enquiry; able to use research skills to create new knowledge that can be applied to real world issues, or contribute to a field of study or practice to enhance society. They will be capable of creative questioning, problem finding and problem solving.
This graduate capability is supported by:
Learning outcomes
- Understand the theory of estimation and sampling distribution;
- Have a solid understanding of hypothesis test and linear regression models;
- Understand and be able to carry out one and two sample tests, and chi-square tests;
- Be able to assess model fit for simple regression models and have a solid understanding of model diagnostics;
- Be able to interpret results from hypothesis tests and linear regression models;
- Use R statistical package to carry out various hypothesis tests, fit simple linear regression models with continuous or categorical covariates, and produce relevant statistical plots/graphs.
Assessment tasks
- Assessed Coursework
- Class test
- Assignment
- Final examination
Changes from Previous Offering
Grading
The Macquarie University grading policy can be found at http://mq.edu.au/policy/docs/grading/policy.html
Note that, in order to be awarded a particular Standardised Numerical Grade (SNG) and Grade, a student must meet the performance standard outlined in the grading policy in both the coursework and the examination sections of the unit.
A Standardised Numerical Grade (SNG) gives you an indication of how you have performed within the band for your descriptive grade. The SNG is not a mark, and you may not be able to work it out based on your raw examination and other assessment marks. Nor are you able to determine you are “one mark away” from a different grade.
Research and Practice
This unit uses research from external sources. References are given in "Required and recommended texts and
materials".
Changes since First Published
| Date | Description |
|---|---|
| 07/08/2014 | In weeks 2 to 12 you will be required to submit tutorial and homework. (In Bold) |
| 07/08/2014 | The Class Test will commence at 10.05 am, Thursday, Week 7. |
| 07/08/2014 | The class test is scheduled in the lecture (Week 7). The assignment submission time has been updated. |
| 31/07/2014 | The Unit Schedule had extras from previous version of Assessment Tasks and this has been fixed now. |