| Semester 2, 2020 On-campus Toowoomba | |
| Short Description: | Statistical Models |
| Units : | 1 |
| Faculty or Section : | Faculty of Health, Engineering and Sciences |
| School or Department : | School of Sciences |
| Student contribution band : | Band 2 |
| ASCED code : | 010103 - Statistics |
| Grading basis : | Graded |
Staffing
Examiner:
Requisites
Pre-requisite: STA3300 or approval of examiner
Rationale
Linear Models and Generalised Linear Models are very widely used statistical tools. Linear models allow us to model data with normally distributed errors and generalised linear models extend these methods to a wider family of distributions. While students are expected to have obtained some understanding of linear regression techniques in previous courses, this course offers a more complete introduction to linear models and their application, then, building on this, extends into generalised linear models. The key functions of linear models are for describing the relationships between variables and predicting outcomes and so inference methods will be addressed in some detail. Finally, as models only give useful information when they provide an accurate reflection of the 'real world', various diagnostic tests on the appropriateness and goodness of fit of various models will be introduced. This course has relevance to all students seeking to pursue a career involving applied statistics.
Synopsis
This course introduces and extends the student's knowledge of linear models. The mathematical development of these models will be considered, however the focus will be on practical applications. The statistical program R will be introduced and used throughout the course. The topics include developing multiple regression models, testing hypotheses for these models, selecting the 'best' model, diagnosing problems in model fit, developing generalised linear models, and a range of applications of generalised linear models including logistic, Poisson and log-linear models.
Objectives
On successful completion of this course students will be able to:
- Recognise appropriate general and generalised linear models for analysis of different types of data sets
- Apply a range of models and diagnostic techniques to test hypotheses and interpret the output correctly and in context.
- Explore the capabilities of and implement R software as a statistical package in analysing different statistical models.
- Interpret and communicate the results of analyses to a diverse audience.
Topics
| Description | Weighting(%) | |
|---|---|---|
| 1. | Review of multiple regression: specifying the model, least squares estimators of regression parameters and variance, maximum likelihood estimators of the regression parameters and variance, multiple and partial correlation, regression through the origin. | 10.00 |
| 2. | Inference on the normal model: interval estimation of the regression parameters and variance, prediction of future responses, analysis of variance, coefficient of determination, tests on single regression coefficients, confidence regions, tests on a subset of the regression coefficients, procedures for model selection, tests on the general linear model, test of goodness fit. | 20.00 |
| 3. | Model selection and checking: criteria for selecting regressors, residual analysis, data transformations, weighted least squares, detecting outliers and influential observations, multicollinearity, detecting multicollinearity. | 20.00 |
| 4. | Generalised linear models: the exponential family of distributions, the mean and variance of the exponential family, specifying the generalised linear model, the link function, estimation of the regression parameters, adequacy of the model, the deviance, analysis of deviance and model selection. | 10.00 |
| 5. | Binary variables and logistic regression: probability distributions, generalised linear models, logistic regression model, deviance, Pearson's Chi-Square test, residuals and other diagnostics. | 20.00 |
| 6. | Count data, Poisson regression and log-linear models: Poisson regression, probability models for contingency tables, log-linear models, inference for log-linear models. | 20.00 |
Text and materials required to be purchased or accessed
ALL textbooks and materials available to be purchased can be sourced from (unless otherwise stated). (https://omnia.usq.edu.au/textbooks/?year=2020&sem=02&subject1=STA3301)
Please for alternative purchase options from USQ Bookshop. (https://omnia.usq.edu.au/info/contact/)
Introductory material (Current Year), Course STA3301 Statistical Models.
(Accessible from the course 精东传媒appDesk.)
(Accessible from the course 精东传媒appDesk.)
精东传媒app Book (current year), Course STA3301 Statistical Models.
(Accessible from the course 精东传媒appDesk.)
(Accessible from the course 精东传媒appDesk.)
All additional study material will be provided on the course 精东传媒appDesk.
Reference materials
Reference materials are materials that, if accessed by students, may improve their knowledge and understanding of the material in the course and enrich their learning experience.
Crawley, MJ 2012, The R Book, 2nd edn, Wiley, New York.
Dobson, AJ and Barnett, AG 2018, An Introduction to Generalized Linear Models, 4th edn, CRC Press, Boca Raton, FL.
McCullagh, P & Nelder, JA 1989, Generalized linear models, 2nd edn, Chapman and Hall, London: New York.
Myers, RH 1990, Classical and modern regression with applications, 2nd edn, Duxbury Press, Belmont.
Student workload expectations
| Activity | Hours |
|---|---|
| Assessments | 32.00 |
| Online Lectures | 26.00 |
| Private 精东传媒app | 88.00 |
| Tutorials | 26.00 |
Assessment details
| Description | Marks out of | Wtg (%) | Due Date | Notes |
|---|---|---|---|---|
| ASSIGNMENT 1 | 20 | 20 | 03 Sep 2020 | (see note 1) |
| ASSIGNMENT 2 | 30 | 30 | 08 Oct 2020 | (see note 2) |
| TAKE HOME EXAMINATION | 50 | 50 | End S2 | (see note 3) |
Notes
- This will be an open examination. Students will be provided further instruction regarding the exam by their course examiner via 精东传媒appDesk. The examination date will be available via UConnect when the official Alternate Assessment Schedule has been released.
Important assessment information
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Attendance requirements:
It is the students' responsibility to participate appropriately in all activities and to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration. -
Requirements for students to complete each assessment item satisfactorily:
Due to COVID-19 the requirements for S2 2020 are:
To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks for that item.
Requirements after S2 2020:
To complete an assessment item satisfactorily, students must obtain at least 50% of the marks available for that assessment item. -
Penalties for late submission of required work:
Students should refer to the Assessment Procedure (point 4.2.4) -
Requirements for student to be awarded a passing grade in the course:
Due to COVID-19 the requirements for S2 2020 are:
To be assured of receiving a passing grade a student must achieve at least 50% of the total weighted marks available for the course.
Requirements after S2 2020:
To be assured of receiving a passing grade a student must obtain at least 50% of the total weighted marks available for the course (i.e. the Primary Hurdle), and have satisfied the Secondary Hurdle (Supervised), i.e. the end of semester examination by achieving at least 40% of the weighted marks available for that assessment item.
Supplementary assessment may be offered where a student has undertaken all of the required summative assessment items and has passed the Primary Hurdle but failed to satisfy the Secondary Hurdle (Supervised), or has satisfied the Secondary Hurdle (Supervised) but failed to achieve a passing Final Grade by 5% or less of the total weighted Marks.
To be awarded a passing grade for a supplementary assessment item (if applicable), a student must achieve at least 50% of the available marks for the supplementary assessment item as per the Assessment Procedure (point 4.4.2). -
Method used to combine assessment results to attain final grade:
The final grades for students will be assigned on the basis of the aggregate of the weighted marks obtained for each of the summative assessment items in the course. -
Examination information:
Due to COVID-19 the requirements for S2 2020 are:
An Open Examination is one in which candidates may have access to any printed or written material and a calculator during the examination.
Requirements after S2 2020:
In a Restricted Examination, candidates are allowed access to specific materials during the examination. The only materials that candidates may use in the restricted examination for this course are: writing materials (non-electronic and free from material which could give the student an unfair advantage in the examination); calculators which cannot hold textual information (students must indicate on their examination paper the make and model of any calculator(s) they use during the examination); Formula sheets and/or Statistical Tables as provided by the Examiner with the examination paper. Students whose first language is not English, may, take an appropriate unmarked non-electronic translation dictionary (but not technical dictionary) into the examination. Dictionaries with any handwritten notes will not be permitted. Translation dictionaries will be subject to perusal and may be removed from the candidate's possession until appropriate disciplinary action is completed if found to contain material that could give the candidate an unfair advantage. -
Examination period when Deferred/Supplementary examinations will be held:
Due to COVID-19 the requirements for S2 2020 are:
The details regarding deferred/supplementary examinations will be communicated at a later date.
Requirements after S2 2020:
Any Deferred or Supplementary examinations for this course will be held during the next examination period. -
精东传媒app Student Policies:
Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene 精东传媒app policies and practices. These policies can be found at .
Other requirements
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Computer, e-mail and Internet access:
Students are required to have access to a personal computer, e-mail capabilities and Internet access to UConnect. Current details of computer requirements can be found at . -
Students can expect that questions in assessment items in this course may draw upon knowledge and skills that they can reasonably be expected to have acquired before enrolling in this course. This includes knowledge contained in pre-requisite courses and appropriate communication, information literacy, analytical, critical thinking, problem solving or numeracy skills. Students who do not possess such knowledge and skills should not expect the same grades as those students who do possess them.