307590 (v.1) Data Evaluation and Experimental Design 101

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Area:	Department of Mathematics and Statistics
Credits:	12.5
Contact Hours:	3.0
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 307591 (v.1) Data Evaluation and Experimental Design 103 or any previous version
Syllabus:	Exploratory data analysis, numerical and graphical summaries of univariate data, and transformations. Graphical evaluation of bi-variate data, normal distribution, normal calculations, and checks for normality. Experiments, random sampling, design of experiments, and introduction to inference. Inference for means, one mean, introduction to inference, and central limit theorem. Checking assumptions, confidence intervals and hypothesis tests using Z and T, and SPSS. Two means, testing of variances, inference for dependent and independent cases, and SPSS. More than two means, ANOVA, and SPSS.
Unit Outcomes:	On completion of this subject students will have - Identified the role of statistics in their own and related discipline areas. Gained understanding of how graphical displays and summary statistics can be used to perform initial diagnosis of data, the basic principles of design of experiment. Designed simple comparative experiments and estimated the sample size required to make comparisons with given precision. Read and interpreted the analysis of data that is typical in research documents in their own and other discipline areas. Demonstrated practical expertise associated with the use of a statistical package in performing basic statistical procedure, and basic knowledge of assessing the appropriateness of statistical models. Demonstrated the use of test and confidence interval for single mean and applied them in their own discipline areas. Distinguished between the different types of statistical tests that may be used to analyse data in different disciplines and gained understanding of subject related practices of tests. Applied decision making processes on the means for more than two populations based on the ANOVA techniques. Analysed real life data by selecting appropriate displays and tests.
Text and references listed above are for your information only and current as of September 30, 2003. Please check with the unit coordinator for up-to-date information.
Unit References:	Coakes S.J, Steed L.G. (2001). Analysis without Anguish, Version 10.0 for Windows. Brisbane, John Wiley and Sons. Walpole, Myers and Myers. (2002). Probability and Statistics for Engineers and Scientists, 7th ed. New Jersey, Prentice Hall. Smith P.J. (1993). Into Statistics. South Melbourne, Nelson. Ross S.M. (1999). Introduction to Probability and Statistics for Engineers and Scientists, 2nd ed. San Diego, Wiley Harcourt/Academic Press. Montgomery D.C., Runger G. C. (1999). Applied Statistics and Probability for Engineers. New York, John Wiley and Sons. Devore J.R. (2000). Probability and Statistics for Engineering and Sciences, 5th ed. Pacific Grove, Duxbury. Sanders and Smidt. (2000). Statistics A First Course, 6th ed. New York, McGraw Hill. Siegel and Morgan, (1996). Statistics and Data Analysis, 2nd ed. New York, Wiley.
Unit Texts:	Moore D. S. and McCabe R.G.P. (1998). Introduction to the Practice of Statistics, 3rd ed. New York, Freeman.
Unit Assessment Breakdown:	Final exam 60%, Module test (1) or assignment (1) 20%, Regular on-line testing 20%. This is a grade/mark assessment.
Year Location Period Internal Area External Central External 2004 Bentley Campus Semester 1 Y     2004 Bentley Campus Semester 2 Y