Module Details


Module Availability
Spring semester
Assessment Pattern
Components of Assessment

Method(s)

Percentage weighting

Coursework

Assignments and tests

25%

Examination

Written Examination (2 hours, unseen)

75%

Prerequisites/Corequisites
MS131 Probability and Statistics
Module Aims
This module provides theoretical background for many of the topics introduced in MS131 and for some of the topics that will appear in subsequent statistics modules.
Learning Outcomes
At the end of the module, a student should:
(1) be familiar with the main results of intermediate distribution theory;
(2) be able to apply this knowledge to suitable problems in statistics.
Module Content
 Review of probability and basic univariate distributions.
 Bivariate and multivariate distributions.
 Transformations.
 Moments, generating functions and inequalities.
 Further discrete and continuous distributions: negative binomial, hypergeometric, multinomial, gamma, beta.
 Univariate, bivariate and multivariate normal distributions.
 Proof of the central limit theorem.
 Distributions associated with the normal distribution: Chisquare, t and F. Application to normal linear models.
 Theory of minimum variance unbiased estimation.
Methods of Teaching/Learning
Teaching is by lectures and tutorials. Learning takes place through lectures, tutorials, exercises and background reading.
3 lecture/tutorial hours per week for 10 weeks
Selected Texts/Journals
Recommended Reading
R.V. Hogg and E.A. Tanis, Probability and Statistical Inference, PrenticeHall, (1997).
Further Reading
A.M. Mood, F.G. Graybill and D.C. Boes, Introduction to the Theory of Statistics, McGrawHill, (1974).
Last Updated
15 August 2006
Module Availability
Spring semester
Assessment Pattern
Unit(s) of Assessment

Weighting Towards Module Mark( %)

2 hour unseen examination




Qualifying Condition(s)
A weighted aggregate mark of 40% is required to pass the module.

Prerequisites/Corequisites
MS131 Probability and Statistics
Module Aims
This module provides theoretical background for many of the topics introduced in MS131 and for some of the topics that will appear in subsequent statistics modules.
Learning Outcomes
At the end of the module, a student should:
(1) be familiar with the main results of intermediate distribution theory;
(2) be able to apply this knowledge to suitable problems in statistics.
Module Content
 Review of probability and basic univariate distributions.
 Bivariate and multivariate distributions.
 Transformations.
 Moments, generating functions and inequalities.
 Further discrete and continuous distributions: negative binomial, hypergeometric, multinomial, gamma, beta.
 Univariate, bivariate and multivariate normal distributions.
 Proof of the central limit theorem.
 Distributions associated with the normal distribution: Chisquare, t and F. Application to normal linear models.
 Theory of minimum variance unbiased estimation.
Methods of Teaching/Learning
Teaching is by lectures and tutorials. Learning takes place through lectures, tutorials, exercises and background reading.
3 lecture/tutorial hours per week for 10 weeks
Selected Texts/Journals
Recommended Reading
R.V. Hogg and E.A. Tanis, Probability and Statistical Inference, PrenticeHall, (1997).
Further Reading
A.M. Mood, F.G. Graybill and D.C. Boes, Introduction to the Theory of Statistics, McGrawHill, (1974).
Last Updated
31 July 2007
Module Availability
Spring Semester
Assessment Pattern
Unit(s) of Assessment

Weighting Towards Module Mark( %)

2 hour unseen examination

75%

Test

10%

Coursework

15%

Qualifying Condition(s)
A weighted aggregate mark of 40% is required to pass the module.

Module Overview
The module gives a presentation of some fundamental mathematical theory underlying statistics.
Prerequisites/Corequisites
The probability component of MAT1017 is prerequisite.
Module Aims
This module provides theoretical background for many of the topics introduced in the probability component of MS125 and for some of the topics that will appear in subsequent statistics modules.
Learning Outcomes
At the end of the module, a student should:
(1) be familiar with the main results of intermediate distribution theory;
(2) be able to apply this knowledge to suitable problems in statistics.
Module Content
Methods of Teaching/Learning
Teaching is by lectures and example classes. Learning takes place through lectures, exercises (example sheets) and background reading.
Selected Texts/Journals
Last Updated
04.11.08
Module Availability
Semester 2
Assessment Pattern
Unit(s) of Assessment

Weighting Towards Module Mark( %)

2 hour unseen examination

75

Test

10

Coursework

15

Qualifying Condition(s)
A weighted aggregate mark of 40% is required to pass the module.

Module Overview
The module gives a presentation of some fundamental mathematical theory underlying statistics.
Prerequisites/Corequisites
The probability component of MAT1025 is a prerequisite.
Module Aims
This module provides theoretical background for many of the topics introduced in the probability component of MS1025 and for some of the topics that will appear in subsequent statistics modules.
Learning Outcomes
At the end of the module, a student should:
(1) be familiar with the main results of intermediate distribution theory;
(2) be able to apply this knowledge to suitable problems in statistics.
Module Content
Review of probability and basic univariate distributions.
Bivariate and multivariate distributions.
Transformations.
Moments, generating functions and inequalities.
Further discrete and continuous distributions: negative binomial, hypergeometric, multinomial, gamma, beta.
Univariate, bivariate and multivariate normal distributions.
Proof of the central limit theorem.
Distributions associated with the normal distribution: Chisquare, t and F.
Application to normal linear models.
Theory of minimum variance unbiased estimation.
Methods of Teaching/Learning
Teaching is by lectures and example classes. Learning takes place through lectures, exercises (example sheets) and background reading.
Selected Texts/Journals
Recommended
J.E. Freund, Mathematical Statistics with Applications, Pearson, (2004).
R.V. Hogg and E.A. Tanis, Probability and Statistical Inference, PrenticeHall, (1997).
Further
A.M. Mood, F.G. Graybill and D.C. Boes, Introduction to the Theory of Statistics, McGrawHill, (1974).
Last Updated
September 10
Module Availability
Semester 1
Assessment Pattern
Unit(s) of Assessment

Weighting Towards Module Mark( %)

2 hour unseen examination

75

2 class tests

25



Qualifying Condition(s)
A weighted aggregate mark of 40% is required to pass the module.

Module Overview
The module gives a presentation of some fundamental mathematical theory underlying statistics.
Prerequisites/Corequisites
MAT1028 Probability
Module Aims
This module provides theoretical background for many of the topics introduced in the probability component of MS1025 and for some of the topics that will appear in subsequent statistics modules.
Learning Outcomes
At the end of the module, a student should:
(1) be familiar with the main results of intermediate distribution theory;
(2) be able to apply this knowledge to suitable problems in statistics.
Module Content
Review of probability and basic univariate distributions.
Bivariate and multivariate distributions.
Transformations.
Moments, generating functions and inequalities.
Further discrete and continuous distributions: negative binomial, hypergeometric, multinomial, gamma, beta.
Univariate, bivariate and multivariate normal distributions.
Proof of the central limit theorem.
Distributions associated with the normal distribution: Chisquare, t and F.
Application to normal linear models.
Theory of minimum variance unbiased estimation.
Methods of Teaching/Learning
Teaching is by lectures and example classes. Learning takes place through lectures, exercises (example sheets) and background reading.
Selected Texts/Journals
Background Reading
J.E. Freund, Mathematical Statistics with Applications, Pearson, (2004).
R.V. Hogg and E.A. Tanis, Probability and Statistical Inference, PrenticeHall, (1997).
A.M. Mood, F.G. Graybill and D.C. Boes, Introduction to the Theory of Statistics, McGrawHill, (1974).
Last Updated
8 June 2011