Students
STAT271 – Statistics I
2014 – S2 Day
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Co-lecturer
Ken Beath
Contact via ken.beath@mq.edu.au
E4A 526
Tuesday 10 - 12
Unit Convenor
Suzanne Curtis
Contact via suzanne.curtis@mq.edu.au
E4A 552
Wednesday 3-4
|
|---|---|
| Credit points |
Credit points
3
|
| Prerequisites |
Prerequisites
STAT272(P)
|
| Corequisites |
Corequisites
|
| Co-badged status |
Co-badged status
|
| Unit description |
Unit description
This is a unit in statistical methods for the analysis of data in which attention is given to the theoretical structure underlying the techniques. It aims to equip students with a wide understanding of statistics such that they are able to employ appropriate methods of analysis in various circumstances. The techniques learned are widely used in the sciences, social sciences, business and many other fields of study. This unit is designed for students majoring in statistics and/or actuarial studies.
Topics include: inference about one and two sample problems using normal theory and non-parametric methods; analysis of variance; multiple comparisons; and regression. |
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:
- Point estimation methods, including the method of moments and maximum likelihood. Properties of estimators. Asymptotic (large sample) properties.
- Sampling distributions and properties of sample statistics. Definition and derivation of t, F and chi-squared distributions etc.
- Interval estimation. Confidence intervals.
- Principles of hypothesis testing. Type I & II errors. Power. Comparison of competing tests. Relationship between confidence intervals and hypothesis testing.
- Confidence intervals and hypothesis testing for the probability parameter in the binomial distribution.
- Confidence intervals and hypothesis testing for location and scale in the single-population case, including related samples (paired comparisons). Classical (normal theory) and nonparametric tests are considered.
- Confidence intervals and hypothesis testing for location and scale in the two-population case. Classical (normal theory) and nonparametric tests are considered.
- Categorical data analysis (chi-squared tests): goodness of fit tests; tests of association; and tests of homogeneity.
- Hypothesis testing for k populations. One-way analysis of variance and nonparametric techniques. Multiple comparisons and contrasts.
- Simple linear regression. Correlation. Multiple linear regression.
- Hypothesis testing for two factor designs (two-way analysis of variance). Contrasts and multiple comparisons.
General Assessment Information
For the two in-class tests, students are permitted to take into the test room one (1) A4 page of personal summary of formulae or notes, written on one or both sides of the page. These notes may be hand-written or typed. You will be permitted to take this sheet with you at the completion of the tests.
Some formulae will be provided (as per statistical tables available on iLearn); all necessary statistical tables will be provided.
Assessment Tasks
| Name | Weighting | Due |
|---|---|---|
| Exam | 60% | University Examination Period |
| Test 1 | 10% | Week 7 |
| Test 2 | 10% | week 11 |
| Assignments | 20% | Weeks 3, 5, 9, 13 |
Exam
Due: University Examination Period
Weighting: 60%
The final examination will be of 3 hours duration with 10 minutes reading time. All material covered in the unit is examinable. Students MUST perform satisfactorily in the final examination in order to pass the unit regardless of their performance throughout the semester.
Relevant statistical tables will be provided at the final examination. These will be the same as the ones made available during the teaching of the unit.
For the final examination you will be permitted to take into the exam room a
nonprogrammable calculator and two (2) A4 pages of formulae or notes, written on one or both sides of the page. These notes may be hand-written or typed. Students should anticipate that they will not be able to take these sheets with them at the completion of the exam, and it is recommended that a photocopy be made if a student has a desire to retain them.
On successful completion you will be able to:
- Point estimation methods, including the method of moments and maximum likelihood. Properties of estimators. Asymptotic (large sample) properties.
- Sampling distributions and properties of sample statistics. Definition and derivation of t, F and chi-squared distributions etc.
- Interval estimation. Confidence intervals.
- Principles of hypothesis testing. Type I & II errors. Power. Comparison of competing tests. Relationship between confidence intervals and hypothesis testing.
- Confidence intervals and hypothesis testing for the probability parameter in the binomial distribution.
- Confidence intervals and hypothesis testing for location and scale in the single-population case, including related samples (paired comparisons). Classical (normal theory) and nonparametric tests are considered.
- Confidence intervals and hypothesis testing for location and scale in the two-population case. Classical (normal theory) and nonparametric tests are considered.
- Categorical data analysis (chi-squared tests): goodness of fit tests; tests of association; and tests of homogeneity.
- Hypothesis testing for k populations. One-way analysis of variance and nonparametric techniques. Multiple comparisons and contrasts.
- Simple linear regression. Correlation. Multiple linear regression.
- Hypothesis testing for two factor designs (two-way analysis of variance). Contrasts and multiple comparisons.
Test 1
Due: Week 7
Weighting: 10%
This test (of 45 minutes duration) will be held during the lecture on Tuesday 16th September 2014, in Week 7.
On successful completion you will be able to:
- Point estimation methods, including the method of moments and maximum likelihood. Properties of estimators. Asymptotic (large sample) properties.
- Sampling distributions and properties of sample statistics. Definition and derivation of t, F and chi-squared distributions etc.
- Interval estimation. Confidence intervals.
- Principles of hypothesis testing. Type I & II errors. Power. Comparison of competing tests. Relationship between confidence intervals and hypothesis testing.
Test 2
Due: week 11
Weighting: 10%
This compulsory test (of 45 minutes duration) will be held during the lecture on Tuesday 28th October 2014, in Week 11.
On successful completion you will be able to:
- Point estimation methods, including the method of moments and maximum likelihood. Properties of estimators. Asymptotic (large sample) properties.
- Sampling distributions and properties of sample statistics. Definition and derivation of t, F and chi-squared distributions etc.
- Interval estimation. Confidence intervals.
- Principles of hypothesis testing. Type I & II errors. Power. Comparison of competing tests. Relationship between confidence intervals and hypothesis testing.
- Confidence intervals and hypothesis testing for the probability parameter in the binomial distribution.
- Confidence intervals and hypothesis testing for location and scale in the single-population case, including related samples (paired comparisons). Classical (normal theory) and nonparametric tests are considered.
- Confidence intervals and hypothesis testing for location and scale in the two-population case. Classical (normal theory) and nonparametric tests are considered.
- Categorical data analysis (chi-squared tests): goodness of fit tests; tests of association; and tests of homogeneity.
Assignments
Due: Weeks 3, 5, 9, 13
Weighting: 20%
There will be four assignments due at approximately fortnightly intervals. Due dates/times and submission details will be specified separately for each assignment. The assignments will be made available at least one week prior to when they are due. Some marks will be allocated for clarity of reasoning and presentation in each assignment.
If a student is unable to submit an assignment on time, they must contact one of the lecturers. Late submissions without approval of the lecturer(s) will not be marked (and given a zero mark). All extension requests must be approved by one of the lecturers. Requests for substantial extensions (one week or more) will require documentary evidence.
On successful completion you will be able to:
- Point estimation methods, including the method of moments and maximum likelihood. Properties of estimators. Asymptotic (large sample) properties.
- Sampling distributions and properties of sample statistics. Definition and derivation of t, F and chi-squared distributions etc.
- Interval estimation. Confidence intervals.
- Principles of hypothesis testing. Type I & II errors. Power. Comparison of competing tests. Relationship between confidence intervals and hypothesis testing.
- Confidence intervals and hypothesis testing for the probability parameter in the binomial distribution.
- Confidence intervals and hypothesis testing for location and scale in the single-population case, including related samples (paired comparisons). Classical (normal theory) and nonparametric tests are considered.
- Confidence intervals and hypothesis testing for location and scale in the two-population case. Classical (normal theory) and nonparametric tests are considered.
- Categorical data analysis (chi-squared tests): goodness of fit tests; tests of association; and tests of homogeneity.
- Hypothesis testing for k populations. One-way analysis of variance and nonparametric techniques. Multiple comparisons and contrasts.
- Simple linear regression. Correlation. Multiple linear regression.
- Hypothesis testing for two factor designs (two-way analysis of variance). Contrasts and multiple comparisons.
Delivery and Resources
Classes
STAT271 is delivered by lectures (3 per week) and tutorials (1 per week, commencing in week 2). All teaching material will be available on iLearn.
Required and Recommended Texts and/or Materials
Recommended: Mendenhall W, Wackerly D and Scheaffer R. “Mathematical Statistics with Applications”, Seventh Edition QA276 .M426 2008
Copies of this book are held in Special Reserve in the University Library. The Library also holds copies of the sixth and previous editions as well as the Student solutions manual.
The following books are useful references for this unit:
| Author(s) | Title | Library Call No. |
| Bain, L.J. & Engelhardt, M. | Introduction to Probability and Mathematical Statistics | QA273.B2546 / 1992 |
| Conover, W.J. | Practical Nonparametric Statistics | QA278.8.C65 / 1999 |
|
Hogg, R.V. & Craig, A.T. |
Introduction to Mathematical Statistics | QA276.H59 / 1995 |
| Larson, H.J. | Introduction to Probability Theory and Statistical Inference | QA273.L352 / 1982 |
| Walpole, R.E. & Myers, R.H. | Probability and Statistics for Engineers and Scientists | TA340.W35 / 1993 |
Tutorial Exercises
Tutorial Exercises and solutions will be available from iLearn at least five days prior to the tutorial. Tutorials will be run as "drop-in" clinics in which students can ask for assistance or further explanation on the tutorial exercises or lecture material. Students may attend as many tutorials as desired.
Technologies Used and Required
Calculators: You will need to equip yourself with a small calculator, preferably one that does simple statistical calculations and/or simple linear regression. You should bring it to all tutorials. You will also need your calculator for the mid-session tests and the final examination. You will not be permitted to use a programmable calculator or one with a full alpha character set in any examination.
Computing and Software: Students will need access to a computer with internet access on a regular basis. Computers are available at various locations on campus for those students who do not own their own. The following software will be used in STAT271:
Minitab (Version 16 or 17): Macquarie University has a license agreement with Minitab which allows students to download a version of Minitab for their computer. Information and instructions for downloading are available from the student portal: https://my.mq.edu.au/. Details of computer rooms set aside for individual student usage can be found at http://www.efs.mq.edu.au/current/ug/resources/labs.
Microsoft Excel and Word (or any other word processor).
Teaching and Learning Strategy
Whilst you are encouraged to discuss the work extensively with your peers, it is expected that the final material submitted will be your own work. Any work that is copied from another student will result in disciplinary action for all students involved.
There is no specific word length for any section of the assessment tasks. Students should note, however, that all real-world problems need to be properly answered. Answers should include definitions of any variables used, the specific hypotheses being tested, a brief rationale for the analysing technique and a meaningful conclusion.
Changes from Previous Unit
None.
Unit Schedule
| Week | Work Due | Submission Details | Value |
| 1 | |||
| 2 | |||
| 3 | Assignment 1 | 5% | |
| 4 | |||
| 5 | Assignment 2 | 5% | |
| 6 | |||
| 7 | Class Test 1 | Tue 16 Sep | 10% |
| Break | Two weeks (Mon 6 Oct = Public Holiday) | ||
| 8 | |||
| 9 | Assignment 3 | 5% | |
| 10 | |||
| 11 | Class Test 2 | Tue 28 Oct | 10% |
| 12 | |||
| 13 | Assignment 4 | Mon 11am | 5% |
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/
All requests for special consideration regarding the mid-session test or final exam must be lodged via the ask@mq facility.
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
Discipline Specific Knowledge and Skills
Our graduates will take with them the intellectual development, depth and breadth of knowledge, scholarly understanding, and specific subject content in their chosen fields to make them competent and confident in their subject or profession. They will be able to demonstrate, where relevant, professional technical competence and meet professional standards. They will be able to articulate the structure of knowledge of their discipline, be able to adapt discipline-specific knowledge to novel situations, and be able to contribute from their discipline to inter-disciplinary solutions to problems.
This graduate capability is supported by:
Learning outcomes
- Point estimation methods, including the method of moments and maximum likelihood. Properties of estimators. Asymptotic (large sample) properties.
- Sampling distributions and properties of sample statistics. Definition and derivation of t, F and chi-squared distributions etc.
- Interval estimation. Confidence intervals.
- Principles of hypothesis testing. Type I & II errors. Power. Comparison of competing tests. Relationship between confidence intervals and hypothesis testing.
- Confidence intervals and hypothesis testing for the probability parameter in the binomial distribution.
- Confidence intervals and hypothesis testing for location and scale in the single-population case, including related samples (paired comparisons). Classical (normal theory) and nonparametric tests are considered.
- Confidence intervals and hypothesis testing for location and scale in the two-population case. Classical (normal theory) and nonparametric tests are considered.
- Categorical data analysis (chi-squared tests): goodness of fit tests; tests of association; and tests of homogeneity.
- Hypothesis testing for k populations. One-way analysis of variance and nonparametric techniques. Multiple comparisons and contrasts.
- Simple linear regression. Correlation. Multiple linear regression.
- Hypothesis testing for two factor designs (two-way analysis of variance). Contrasts and multiple comparisons.
Assessment tasks
- Exam
- Test 1
- Test 2
- Assignments
Critical, Analytical and Integrative Thinking
We want our graduates to be capable of reasoning, questioning and analysing, and to integrate and synthesise learning and knowledge from a range of sources and environments; to be able to critique constraints, assumptions and limitations; to be able to think independently and systemically in relation to scholarly activity, in the workplace, and in the world. We want them to have a level of scientific and information technology literacy.
This graduate capability is supported by:
Assessment task
- Assignments
Problem Solving and Research Capability
Our graduates should be capable of researching; of analysing, and interpreting and assessing data and information in various forms; of drawing connections across fields of knowledge; and they should be able to relate their knowledge to complex situations at work or in the world, in order to diagnose and solve problems. We want them to have the confidence to take the initiative in doing so, within an awareness of their own limitations.
This graduate capability is supported by:
Assessment task
- Assignments
Effective Communication
We want to develop in our students the ability to communicate and convey their views in forms effective with different audiences. We want our graduates to take with them the capability to read, listen, question, gather and evaluate information resources in a variety of formats, assess, write clearly, speak effectively, and to use visual communication and communication technologies as appropriate.
This graduate capability is supported by:
Assessment task
- Assignments
Changes since First Published
| Date | Description |
|---|---|
| 29/07/2014 | Changed assignment number to four. |
| 28/02/2014 | The Description was updated. |