7149 (v.7) Principles of Statistics 101

Area:

Department of Mathematics and Statistics

Credits:

25.0

Contact Hours:

4.0

Lecture:

1 x 2 Hours Weekly

Tutorial:

1 x 2 Hours Weekly

Anti Requisite(s):

10925 (v.3) Principles of Statistics 103 or any previous version
307590 (v.1) Data Evaluation and Experimental Design 101 or any previous version
307591 (v.1) Data Evaluation and Experimental Design 103 or any previous version

Syllabus:

Designed for students with TEE Applicable Mathematics. Includes study of inference for means - One Mean - introduction to inference, central limit theorem, checking assumptions, confidence intervals and hypothesis tests using Z and T, SPSS. Two means - testing of variances, inference for dependent and independent cases, SPSS. More than two means, ANOVA, SPSS. Module 4, qualitative data. Binomial - recognising binomial situations, calculations. Proportions - Inference for one and two proportions. Chi-squared distribution - tests for independence, homogeneity, goodness of fit of binomial model. Logistic Regression - fitting and interpreting the model.

Unit Outcomes:

On completion of this unit, students will have - Demonstrated an understanding of basic probability concepts including Bayes' theorem. Performed modeling using various distributions. Performed probability calculations associated with bivariate distributions. Gained understanding of and used functions of random variables. Identified the role of statistics in their own and related discipline areas. Used graphical and numerical displays to perform initial diagnosis of data. Gained understanding the basic principles of design of experiment and designed simple comparative experiments. Demonstrated practical expertise associated with the use of a statistical package in performing basic statistical procedures. Demonstrated basic knowledge of assessing the appropriateness of statistical models. Demonstrated the use of test and confidence interval for means and applied them in their own discipline areas. Applied decision making process on the means for more than two populations based on the analysis of variance (ANOVA) techniques. Analysed categorical data and drawn appropriate conclusions relating to proportions. Analysed cross classified frequency data. Used Pearson's Chi square test. Completed categorical data analysis using logistic regression.

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 LG.(2001). Analysis without Anguish. Version 10.0 for windows. Brisbane, John Wiley and Sons. Moore D. S. and McCabe R.G.P. (1998). Introduction to the Practice of Statistics, 3rd ed. New York, Freeman. 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. Montgomery D.C., Runger G. C. (1999). Applied Statistics and Probability for Engineers. New York, John Wiley and Sons.

Unit Texts:

R E Walpole, R H Myers and S L Myers. 2002. Probability and Statistics for Engineers and Scientists 7th edition Prentice Hall. Mathematical Formulae and Statistical tables for use at tertiary Institutions. P Hollis and M G Nair Principles of Statistics101 Lecture Notes. Curtin Publication

Unit Assessment Breakdown:

Project (written) 5%+5%, Project (Presentation) 5%, Quizzes 20%, Mid-semester Test 15%, Final Exam 50%. This is by grade/mark assessment.

Year	Location	Period	Internal	Area External	Central External
2004	Bentley Campus	Semester 1	Y
2004	Bentley Campus	Semester 2	Y
2004	Miri Sarawak Campus	Semester 1	Y
2004	Miri Sarawak Campus	Semester 2	Y