307591 (v.1) Data Evaluation and Experimental Design 103

Area:	Department of Mathematics and Statistics
Credits:	12.5
Contact Hours:	3.0

The tuition pattern below provides details of the types of classes and their duration. This is to be used as a guide only. For more precise information please check your unit outline.

Lecture:	1 x 2 Hours Weekly
Tutorial:	1 x 1 Hours Weekly
Anti Requisite(s):	7149 (v.7) Principles of Statistics 101 or any previous version 10925 (v.3) Principles of Statistics 103 or any previous version 307590 (v.1) Data Evaluation and Experimental Design 101 or any previous version

Syllabus:	Designed for students with TEE Applicable Mathematics. Regression Analysis. Simple linear regression - measures of model adequacy, residual analysis, transformations, Inference for slope and intercept. Confidence and prediction intervals for future responses. Multiple linear regression - estimation of model parameters, inference regarding model parameters and predictions, ANOVA, regression diagnostics, variable selection and model building. Non parametric methods. Introduction to non parametric analysis. Non - parametric tests - Sign test, Signed-rank test, Rank-sum test, Runs test. Kruskal Wallace test, Rank correlation coefficient Checking distributions - Q-Q plots and Kolmogorov Smirnov.

To ensure that the most up-to-date information about unit references, texts and outcomes appears, they will be provided in your unit outline prior to commencement.

Field of Education:	10101 Mathematics
Funding Cluster:	04 - Mathematics, Statistics
SOLT (Online) Definitions*:	Essential *Extent to which this unit or thesis utilises online information
Result Type:	Grade/Mark
Availability
Availability Information has not been provided by the respective School or Area. Prospective students should contact the School or Area listed above for further information.

307591 (v.1) Data Evaluation and Experimental Design 103

Availability