# STA404 Statistical Data Analysis (8)

This subject explores the use of statistical techniques to answer questions posed by researchers. It starts by determining the types of data to be collected and how this should be done to maximise the information obtained. It then considers various methods of statistical data analysis that might be required and considers their underlying assumptions, the situations in which they may be appropriate and what results can be obtained. Emphasis will be placed on interpreting these results, communicating with colleagues and using a statistical package.The methods of design and analysis will be illustrated by case studies. The learning will be motivated by applying the subject material to a practical experiment.

## Availability

Session 1 (30)
Online
Wagga Wagga Campus

Continuing students should consult the SAL for current offering details: STA404. Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

## Subject Information

HD/FL

One session

##### School

School of Computing and Mathematics

STA201

## Learning Outcomes

##### Upon successful completion of this subject, students should:
• be able to describe the role of statistical methodology in scientific investigations;
• be able to explain the fundamental differences between observational studies and experimental designs, including the advantages and disadvantages of each;
• be able to design a data collection process in consultation with colleagues so as to maximise the information obtained for a specified cost;
• be able to identify and apply appropriate methods of statistical analysis to data that arise in a variety of research situations;
• be able to interpret the results of a statistical analysis and be able to explain the conclusions to someone without statistical training; and
• be able to use a commonly available statistical computer package to perform the analyses.

## Syllabus

##### This subject will cover the following topics:
• Brief introduction or revision of t-tests, one-way Analysis of Variance (ANOVA) and use of a statistical package.
• Design and analysis of factorial designs, split-plot designs and repeated measures designs.
• Generalised linear models, including logistic regression and Poisson (or `log-linear') regression.
• Multivariate techniques, including multivariate graphical techniques and multivariate ANOVA.
• Project planning.