Units

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Sample subject outline - Semester 1 2013

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

Rationale

Fundamental quantitative and related skills for the collection, handling, exploration, analysis and interpretation of data and variation are vital for any discipline and in any society which uses data. Statistical reasoning and data analysis underlie many fields of science at advanced levels. Basic quantitative methods support statistical data analysis.

Aims

The aim of this unit is to provide you with an essential grounding in statistical reasoning, and in basic methods for the analysis of data and interpretation of variation in all areas of modern science, social science, technology, industry and associated fields. The unit also provides you with key statistical knowledge to apply in many advanced units and projects which involve data and influences of random variation. Fundamental quantitative methods which inform and support statistical knowledge are also provided.

Objectives

Successful completion of this unit will enable the following:

1. Independently demonstrate your knowledge and understanding of a range of standard basic quantitative and statistical methods and in what contexts it is appropriate to apply them.

2. Identify, apply and justify appropriate quantitative methods to analyse quantitative data and solve quantitative problems in a range of contexts, using appropriate software where required.

3. Understand the importance, in scientific, technological, social science, business and related fields, of responsible and accountable collection, analysis and reporting of data and variation.

Content

Exploratory Data Analysis
· Looking at data: distributions
· Looking at data: relationships

Data Collection and Randomness
· Producing data
· Randomness and sampling distributions

Analysis of Continuous Data
· Introduction to inference
· Inference for distributions
· One-way and two-way analysis of variance
· Simple and multiple regression
· Interpolation methods
· Analysis of curves

Analysis of Categorical Data
· Inference for proportions
· Analysis of two-way tables

Illustrations of content will be based on various discipline areas.

Approaches to Teaching and Learning

Lectures will be preceded by prescribed reading, which will form the basis of discussion, exposition, homework review, worked examples, examples for you to work, and internet- and computer-based demonstrations in the lectures. Practical classes will comprise further homework review, one-to-one discussion on progress with homework and lecture and reading materials, and guided use of statistical computing packages to analyse data. Regular assignments will focus your reading in advance of lectures. Worked solutions to homework will enable you to have timely feedback on that aspect of your private study. Practical work will synthesise concepts, techniques, and skills discussed in lectures. Project work will enable you to bring together topics in this unit through investigation, analysis and reporting, problem-solving and communication skills.

Assessment

In general, assessment is designed to give you progressive feedback during semester on your acquisition of capabilities. The range of assessment types reflects the relative importance of statistical problem solving, knowledge of relevant basic content, and skills in applying data analytical methods.Assessment carried out during the teaching period will be marked, with written comments, and returned promptly after submission deadlines as required by University policy. Examination scripts can be perused after the release of official results in the unit. The lecturer and demonstrators will be reasonably available for consultation in case of queries over marking of assessment items.

Assessment name: Problem Solving Task
Description: (Formative and Summative). These regular quizzes cover the core knowledge and skills of the unit and provide you with an excellent way of learning the core content and skills.
Relates to objectives: All
Weight: 30%
Internal or external: Internal
Group or individual: Individual
Due date: Regularly during sem

Assessment name: Project (Applied)
Description: (Formative and Summative). Statistical data analysis project involving identification of questions of interest; planning, organising, handling of data; exploration, presentation, analysis of data; reporting in context. The project may be divided into parts, which may have regular in-semester due dates: this will be confirmed during the first week of classes. The project may be assigned as a group project: this will be confirmed during the first week of classes.
Relates to objectives: All
Weight: 20%
Internal or external: Internal
Group or individual: Individual
Due date: Late in semester

Assessment name: Examination
Description: (Summative). You are required to undertake an examination at approximately the end of semester, possibly on a Saturday, based on all the material covered in the semester. The examination may be held during the examination period: this will be confirmed during the first week of classes. You may also be required to undertake part of the examination in approximately the middle of semester, possibly on a Saturday: this will be confirmed during the first week of classes. Lecture examples, practical class exercises, homework, and quizzes form exemplars for the examination. Feedback is available through review of examination papers on request.
Relates to objectives: All
Weight: 50%
Internal or external: Internal
Group or individual: Individual
Due date: Approx. end semester

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

Text:
1. Moore DS, McCabe GP & Craig B (2012) Introduction to the Practice of Statistics (with CD) (Extended Version), 7th edition, New York: WH Freeman


References:
1. Moore DS, McCabe GP & Craig B (2009) Introduction to the Practice of Statistics (with CD) (Extended Version), 6th edition, New York: WH Freeman
2. Utts JM & Heckard RF (2007) Mind on Statistics, 3rd edition, CA: Thomson Brooks/Cole
3. Seber GAF & Wild CJ (2000) Chance Encounters: A First Course in Data Analysis and Inference, New York: John Wiley
4. Salsburg D (2001) The Lady Tasting Tea: How Statistics Revolutionised Science in the Twentieth Century, New York: WH Freeman
5. Adams RA & Essex C (2010) Calculus: A Complete Course, 7th edition, Toronto: Pearson
6. Stewart J (2008) Calculus, 6th edition, CA: Thomson Brooks/Cole
7. Anton H, Bivens I & Davis S (2005) Calculus, 8th edition, New York: Wiley

Risk assessment statement

There are no out-of-the-ordinary risks associated with undertaking 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: 10-May-2012