Students
STAT271 – Statistics I
2015 – S2 Day
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Associate Lecturer
Suzanne Curtis
AHH, Level 2, Workstation 35
To be determined
|
|---|---|
| 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 and Type 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 for a single-population, 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.
- Correlation and linear regression. Model fitting and inference for simple and multiple linear regression.
- Hypothesis testing for k populations. One-way analysis of variance and nonparametric techniques. Multiple comparisons and contrasts.
- Hypothesis testing for two factor designs (two-way analysis of variance) - normal theory and nonparametric techniques. Multiple comparisons and contrasts.
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 handwritten 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 One | 10% | week 7 |
| Test Two | 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. 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 will not be permitted to take these 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 and Type 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 for a single-population, 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.
- Correlation and linear regression. Model fitting and inference for simple and multiple linear regression.
- Hypothesis testing for k populations. One-way analysis of variance and nonparametric techniques. Multiple comparisons and contrasts.
- Hypothesis testing for two factor designs (two-way analysis of variance) - normal theory and nonparametric techniques. Multiple comparisons and contrasts.
Test One
Due: week 7
Weighting: 10%
This compulsory test (of 45 minutes duration) will be held during a lecture hour in Week 7. The specific date is yet to be determined.
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 and Type II errors. Power. Comparison of competing tests. Relationship between confidence intervals and hypothesis testing.
Test Two
Due: Week 11
Weighting: 10%
This compulsory test (of 45 minutes duration) will be held during a lecture hour in Week 11. The specific date is yet to be determined.
On successful completion you will be able to:
- Confidence intervals and hypothesis testing for the probability parameter in the binomial distribution.
- Confidence intervals and hypothesis testing for location and scale for a single-population, 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 the lecturer. Late submissions without approval of the lecturer will not be marked (and given a zero mark). All extension requests must be approved by the lecturer. 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 and Type 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 for a single-population, 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.
- Correlation and linear regression. Model fitting and inference for simple and multiple linear regression.
- Hypothesis testing for k populations. One-way analysis of variance and nonparametric techniques. Multiple comparisons and contrasts.
- Hypothesis testing for two factor designs (two-way analysis of variance) - normal theory and nonparametric techniques. Multiple comparisons and contrasts.
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:
| Authors | 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 |
Learning and Teaching Activities
Lectures
Tutorials
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/
Results
Results shown in iLearn, or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au.
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 and Type 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 for a single-population, 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.
- Correlation and linear regression. Model fitting and inference for simple and multiple linear regression.
- Hypothesis testing for k populations. One-way analysis of variance and nonparametric techniques. Multiple comparisons and contrasts.
- Hypothesis testing for two factor designs (two-way analysis of variance) - normal theory and nonparametric techniques. Multiple comparisons and contrasts.
Assessment tasks
- Exam
- Test One
- Test Two
- 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 tasks
- Exam
- Test One
- Test Two
- Assignments