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
10925 (v.3) Principles of Statistics 103 or any previous version
307590 (v.1) Data Evaluation and Experimental Design 101 or any previous version
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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. |
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Unit Outcomes: | On completion of this subject students will be able to: analyse relationships between variables using linear regression, including transformations; analyse multiple linear regression models using parameter estimation, ANOVA tables and hypothesis tests; explain and apply regression diagnostics and variable selection methods; explain the differences between parametric and non-parametric methods; apply non-parametric tests Sign test; Signed-rank test; Rank-sum test and Kruskal Wallace test in appropriate situations; apply runs test to test for randomness; explain the concept of rank correlation and test for its significance; explain the concept of empirical distribution function and Kolmogorov Smirnov test; apply Kolmogorov Smirnov test to test for goodness of fit in one and two sample situations. |
Texts and references listed below are for your information only and current as of September 30, 2003. Some units taught offshore are modified at selected locations. Please check with the unit coordinator for up-to-date information and approved offshore variations to unit information before finalising study and textbook purchases. |
Unit References: | Coakes SJ, 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. (0-7167-3502-4). Smith P.J. (1993). Into statistics, South Melbourne. Nelson. (0 17 008710 7). Ross S.M. (1999). Introduction to Probability and Statistics for Engineers and Scientists, 2nd ed, San Diego, Wiley. (0125984723). Montgomery D.C., Runger G. C. (1999). Applied Statistics and Probability for Engineers. New York., John Wiley and Sons. (0 471 17027 5). |
Unit Texts: | Walpole, Myers and Myers. (2002). Probability and Statistics for Engineers and Scientists, 7th ed. New Jersey. Prentice Hall. |
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Unit Assessment Breakdown: | Final Exam 60%, Module Tests (1) or Assignments (1) 20%, Regular On-line Testing 20%. This is by grade/mark assessment. |
Field of Education: | 10100 Mathematical Sciences (Narrow Grouping) | HECS Band (if applicable): | 2 |
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Extent to which this unit or thesis utilises online information: | Essential | Result Type: | Grade/Mark |
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Availability
| Year | Location | Period | Internal | Area External | Central External | | 2004 | Bentley Campus | Semester 1 | Y | | | | 2004 | Bentley Campus | Semester 2 | Y | | |
Area
External | refers to external course/units run by the School or Department, offered online or through Web CT, or offered by research. |
Central
External | refers to external course/units run through the Curtin Bentley-based Distance Education Area |
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