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| Module Availability |
| Spring |
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| Assessment Pattern |
| Unit(s) of Assessment |
Weighting Towards Module Mark (%)
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| 2 hour Examination |
70
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| Coursework 1 |
15
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| Coursework 2 |
15
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Qualifying Condition(s)
A weighted aggregate mark of 40% is required to pass this module |
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| Module Overview |
This module covers probability and statistical inference thsu enabling students to undertake empirical work. The emphasis is on applications rather than mathematical rigour |
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| Prerequisites/Co-requisites |
| None |
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| Module Aims |
- to further develop good data handling skills
- to understand the role statistical inference plays in economic analysis
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| Learning Outcomes |
By the end of the module students will:-
- have a good understanding of probability concepts including both discrete and continuous probability distibutions
- be able to construct and interpret confidence intervals
- be able to formulate and conduct hypothesis tests for a wide range of applications
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| Module Content |
The following is an indication of the likely topics to be covered:-
- probability - mutually exclusive, independent and dependent events: Binomial, Poisson, Normal, t and Chi-squared distributions
- Statistical inference - random samples; sampling distributions; confidence intervals and hypothesis testing
- Covariance, correlation and expectation
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| Methods of Teaching/Learning |
| Lectures (11) and Classes (5) |
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| Selected Texts/Journals |
Lind, Marchal & Wathen, Basic Statistics for Business & Economics, 7th edition, McGraw Hill, 2010
Anderson, Sweeney, Williams, Freeman & Shoesmith, Statistics for Business and Economics, thomson, 2007
Crawshaw & Chambers, Consise Course in Advanced Level Statistic, 4th edition, Nelson Thomas, 2001 |
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| Last Updated |
10 March 2011 |
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