Data Analysis 1

This subject has the following teaching availabilities in 2012:

Lectures, practice classes and computer laboratory classes.

Estimated total time commitment of 120 hours

Study score of 25 or more in VCE Mathematical Methods 3/4 or equivalent, or

None

None

Students may only gain credit for one of

• MAST10010 Data Analysis 1
• MAST10011 Experimental Design and Data Analysis
• ECON10005 Quantitative Methods 1
• 620-152 Introduction to Biomedical Statistics (prior to 2008)
• 620-160 Experimental Design and Data Analysis (prior to 2008

Students who have completed any of the following may not enrol in this subject for credit

• MAST20005 Statistics
• MAST20017 Applied Statistics for Optometrists (prior to 2012)
• 620-270 Applied Statistics (prior to 2009)

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/

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:

• Understand the importance of random samples and experimental design in scientific research;
• Understand some fundamental concepts of statistical inference relating to confidence intervals and hypothesis testing;
• Use quantitative and graphical methods to describe a set of data;
• Develop expertise in the use of some common statistical techniques;
• Become familiar with a major statistical computing package.

Eight to ten online quizzes 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 based test in the second half of semester (5%), and a 3-hour written examination in the examination period (80%).

Jessica Utts and Robert Heckard, Mind on Statistics, 4th Edition, Cengage Learning, 2010.

This subject potentially can be taken as a breadth subject component for the following courses:

• Bachelor of Arts
• Bachelor of Commerce
• Bachelor of Environments
• Bachelor of Music

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.

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:

• problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;

• analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;

• collaborative skills: the ability to work in a team;

• time-management skills: the ability to meet regular deadlines while balancing competing commitments; and

• computer skills: the ability to use statistical computing packages.

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.