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| Module Availability |
| Semester 2 |
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| Assessment Pattern |
Assessment Pattern
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Unit(s) of Assessment
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Weighting Towards
Module Mark( %)
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Tests: 1 or more
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25
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Exam: End of Spring semester
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75
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Qualifying Condition(s)
A weighted aggregate mark of 40% is required to pass the module.
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| Module Overview |
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| Prerequisites/Co-requisites |
| None. |
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| Module Aims |
This module introduces students to mathematical probability theory and its applications to statistics. |
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| Learning Outcomes |
By the end of this module a student should be able to:
• Apply basic results in probability, distribution theory and statistical inference to straightforward problems.
• Use the statistical package R to perform statistical calculations and to produce numerical and graphical data summaries.
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| Module Content |
• Probability theory, including Bayes' Theorem.
• Some standard discrete distributions: binomial, Poisson, hypergeometric, and continuous distributions: normal, exponential.
• Expectation and moments.
• Probability generating functions.
• Sums of random variables.
• Statement of the central limit theorem.
• Sampling distributions.
• Inference for means and proportions.
• Estimation: unbiased estimators; maximum likelihood and moment estimators.
• Hypothesis testing: Type I and II errors, power.
• Chi-squared Goodness of fit.
• An Introduction to R.
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| Methods of Teaching/Learning |
Teaching is by lectures, tutorials, computer lab sessions and tests. Learning takes place through lectures, tutorials, computer lab sessions, tests and exercise sheets.
40 hours of lectures, tutorials and lab sessions over 12 weeks in the Spring semester.
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| Selected Texts/Journals |
Recommended Reading:
• G.M. Clarke and D. Cooke, A Basic Course in Statistics, Arnold
• John E. Freund, Mathematical Statistics with Applications, Prentice Hall
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| Last Updated |
| 21 April 2011 |
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