Note: This is an archived Handbook entry from 2011.
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2011:Semester 2, Parkville - Taught on campus.
Lectures, practice classes and computer laboratory classes.
Timetable can be viewed here. For information about these dates, click here.
|Time Commitment:||Contact Hours: 3 x one hour lectures per week, 1 x one hour practice class per week, 1 x one hour computer laboratory class per week. |
Total Time Commitment: Estimated total time commitment of 120 hours
Study score of 25 or more in VCE Mathematical Methods 3/4 or equivalent, or
Study Period Commencement:
|Recommended Background Knowledge:||None|
|Non Allowed Subjects:||
Students may only gain credit for one of
Students who have completed any of the following may not enrol in this subject for credit
|Core Participation Requirements:||For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry. |
The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit website: http://www.services.unimelb.edu.au/disability/
CoordinatorMs Sharon Gunn
First Year Coordinator
This subject lays the foundations for an understanding of the fundamental concepts of probability and statistics required for data analysis. Students should develop expertise in some of the statistical techniques commonly used in the design and analysis of experiments, and will gain experience in the use of a major statistical computing package. They should develop skills in collecting random samples, data description, basic statistical inference including parametric and nonparametric tests to compare population proportions and means, data manipulation and statistical computing. The methods will be illustrated using applications from science, engineering and commerce. Descriptive statistics, data manipulation and the implementation of the statistical procedures covered in lectures will be reinforced in the computer laboratory classes.
Sampling; introduction to experimental design; review of simple probability; estimation; confidence intervals; hypothesis testing including types of errors and power; inferences about means and proportions based on single and independent samples; matched pairs designs; introduction to nonparametric methods; contingency tables; regression; and analysis of variance.
Students completing this subject should:
Ten or eleven homework quizzes (online) due at weekly intervals during semester (5%), two or three written assignments due at regular intervals during semester amounting to a total of up to 25 pages (10%), one 45-minute computer laboratory test towards the end of semester (5%), and a 3-hour written examination in the examination period (80%).
|Prescribed Texts:||J. M. Utts and R. F. Heckard, Mind on Statistics 3rd edn, Duxbury, USA, 2007.|
|Breadth Options:|| |
This subject potentially can be taken as a breadth subject component for the following courses:
You should visit learn more about breadth subjects and read the breadth requirements for your degree, and should discuss your choice with your student adviser, before deciding on your subjects.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:
This subject is available for science credit to students enrolled in the BSc (both pre-2008 and new degrees), BASc or a combined BSc course.
Bachelor of Science |
Science credit subjects* for pre-2008 BSc, BASc and combined degree science courses
|Related Breadth Track(s):||
Data Analysis |
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