Units

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Sample subject outline - Semester 2 2013

Note: Subject outlines often change before the semester begins. Below is a sample outline.

Rationale

The application of statistical techniques and methodologies is ubiquitous in practice. The solution to statistical problems in Engineering, Science, Health and Business for example requires individuals with a strong background in applied information analysis.

Aims

This unit is intended for students who have completed foundation studies in Statistics and who wish to develop further skills in applied information analysis. Its exploration of regression, including consideration of covariates, model selection, sampling theory and experimental design will be of interest to students undertaking either of the mathematics majors as well as students in other disciplines who need to quantitatively summarise and analyse data and/or collect data through controlled experiments. Focus is on the application of the theoretical building blocks that are constructed as part of the unit and this is enhanced by the introduction to and extensive use of mathematical and statistical software packages. The unit leads to further study for students who wish to obtain a strong background in applied information analysis.

Objectives

Successful completion of this unit should enable you to:

1. Fit and assess statistical models of independent data via model formulation and parametric estimation.

2. Develop linear statistical models relating a response variable to specified covariates via regression, and use these models for the purposes of prediction and understanding.

3. Have an understanding of the statistical considerations in designing experiments, and be able to use these to fit linear statistical models to such experimental data.

4. Enhance the following generic capabilities:
(a) Communicate in writing, graphically and orally appropriate to context.
(b) Apply knowledge in practical situations.
(c) Engage analytical thinking skills
(d) Work in a team and collaborate with fellow workers.
(e) Draw on a range of knowledge and thinking skills to solve problems.

Content

Parametric estimation, estimating relationships via linear regression and linear models; analysis of the method of least squares; basic inference and model choice; forecasting models and application; introduction to sampling methods in a practical context; models for categorical data; introduction to the design of experiments; ANOVA, non-parametric techniques, including Bootstrapping.

Approaches to Teaching and Learning

There will be a two hour lecture and a two hour practical each week. Lectures will generally develop and extend what is in the printed notes. In the two hour practical class, there will be a combination of lecture and practical material, and all students will have a PC to work through problems. In the practical sessions, lecture material will be presented and developed where necessary.

Assessment

Formative assessment will take the form of feedback on weekly practical session problems. Exercises will be handed out and some of these done in the practical session. Problem Solving Tasks will closely follow the material covered in these sessions, so attempting practical session problems is highly recommended.Formative feedback is provided throughout the semester on individual, group and class work in verbal and written forms. Timely summative feedback is provided on all individual and group assessment, consisting of comments to assist students improve their understanding and problem-solving skills, and model solutions to all exercises and problems.

Assessment name: Problem Solving Task
Description: This will consist of two problem solving exercises based on class and laboratory work, the first due at the end of week 5 and the second due at the end of week 9. The exercises will be handed out two weeks before they are due. The exercises will be marked and will help you with both understanding and communication skills. The marked exercises, feedback and suggested solutions will be given to you within 2 weeks of submission of each exercise.. Summative
Relates to objectives: 1, 2 and 3
Weight: 25%
Internal or external: Internal
Group or individual: Individual
Due date: Week 5 & 9

Assessment name: Project (applied)
Description: Whole semester group project on context of your choice; identification of questions of interest; planning, collection, handling of data; exploration, presentation, analysis of data; reporting in context. Group presentation and project report. Summative
Relates to objectives: 1, 2,3
Weight: 25%
Internal or external: Internal
Group or individual: Group
Due date: Weeks 12 & 13

Assessment name: Examination (Theory)
Description: There will be information sheets allowed, and a significant amount of the material will require the understanding of output from the computing packages R used in the unit. Summative
Relates to objectives: 1, 2 and 3.
Weight: 50%
Internal or external: Internal
Group or individual: Individual
Due date: Exam Period

Academic Honesty

QUT is committed to maintaining high academic standards to protect the value of its qualifications. To assist you in assuring the academic integrity of your assessment you are encouraged to make use of the support materials and services available to help you consider and check your assessment items. Important information about the university's approach to academic integrity of assessment is on your unit Blackboard site.

A breach of academic integrity is regarded as Student Misconduct and can lead to the imposition of penalties.

Resource materials

Learning resources will be made available to students throughout the semester, via Blackboard. A useful reference text includes: Mendenhall and Sincich, A Second Course in Statistics: Regression Analysis, Pearson.

Risk assessment statement

There are no out of the ordinary risks associated with this unit.

Disclaimer - Offer of some units is subject to viability, and information in these Unit Outlines is subject to change prior to commencement of semester.

Last modified: 12-Feb-2013