UCC Book of Modules, 2012/2013: STXXXX

Credit Weighting: 5

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students: Min 5.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Practicals.

Module Co-ordinator: Dr Tony Fitzgerald, Department of Epidemiology and Public Health (Department of Statistics).

Lecturer(s): Dr Tony Fitzgerald, Department of Epidemiology and Public Health.

Module Objective: Provide an understanding of the theory and application of statistical methods in health sciences. Introduce students to the research process concentrating on 'population' based studies with examples from recent research.

Module Content: The application of Statistical Methods in Health Sciences; Descriptive Statistics and Graphical Representations; Descriptive and analytical study designs; Measures of disease-exposure association; Evaluation of diagnostic and screening tests; Estimation and hypothesis testing; Casual inference.

Learning Outcomes: On successful completion of this module, students should be able to:
· Describe and summarise quantitative data using frequency tables, numerical measures, graphs;
· Discuss various measures of disease-exposure association used in epidemiolgical studies. Estimate and interpret these measures using real data;
· Discuss the statistical techniques used in the evaluation of diagnostic and screening test. Calculate and interpret appropriate summary statistics;
· Explain the concept of sampling variation which underlies significance testing and confidence interval estimation;
· Explain the concept of casual inference and apply statistical techniques to identify confounding and effect modification in epidemiological studies;
· Perform fundamental statistical analysis using SPSS?;
· Critically appraise epidemiological studies and published literature.

Assessment: Total Marks 100: End of Year Written Examination 70 marks; Continuous Assessment 30 marks (3 in practical SPSS exams (3 x 4 marks each), 2 take-home assignments (2 x 9 marks each)).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 50%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn.

Credit Weighting: 5

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students: Max 50.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Practicals.

Module Co-ordinator: Dr Tony Fitzgerald, Department of Epidemiology and Public Health (Department of Statistics).

Lecturer(s): Dr Tony Fitzgerald, Department of Epidemiology and Public Health.

Module Objective: Provide an understanding of the theory and application of statistical methods in health sciences. Introduce students to the research process concentrating on 'population' based studies with examples from recent research.

Module Content: The application of Statistical Methods in Health Sciences; Descriptive Statistics and Graphical Representations; Descriptive and analytical study designs; Measures of disease-exposure association; Evaluation of diagnostic and screening tests; Estimation and hypothesis testing; Casual inference.

Learning Outcomes: On successful completion of this module, students should be able to:
· Describe and summarise quantitative data using frequency tables, numerical measures, graphs;
· Discuss various measures of disease-exposure association used in epidemiolgical studies. Estimate and interpret these measures using real data;
· Discuss the statistical techniques used in the evaluation of diagnostic and screening test. Calculate and interpret appropriate summary statistics;
· Explain the concept of sampling variation which underlies significance testing and confidence interval estimation;
· Explain the concept of casual inference and apply statistical techniques to identify confounding and effect modification in epidemiological studies;
· Perform fundamental statistical analysis using SPSS?;
· Critically appraise epidemiological studies and published literature.

Assessment: Total Marks 100: End of Year Written Examination 70 marks; Continuous Assessment 30 marks (3 in practical SPSS exams (3 x 4 marks each), 2 take-home assignments (2 x 9 marks each)).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 10

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students: Min 50, Max 200.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 48 x 1hr(s) Lectures; 20 x 1hr(s) Tutorials; 20 x 1hr(s) Practicals.

Module Co-ordinator: Dr Michael Cronin, Department of Statistics.

Lecturer(s): Dr Michael Cronin, Department of Statistics.

Module Objective: To provide an understanding of the basic methods and techniques of business statistics.

Module Content: Data collection and sampling methods. Organising and presenting data. Measures of location and spread. Introduction to probability. Discrete probability distributions, including Bernoulli, Binomial and Poisson. Continuous probability distributions, including Normal and Uniform. Sampling distributions. Statistical estimation in large samples. Statistical Estimation in Small Samples. Introduction to Hypothesis testing. Simple linear regression analysis. Introduction to statistical process control. Time series analysis and forecasting. Contingency tables and goodness of fit tests. Statistical analysis and reporting using Microsoft Excel.

Learning Outcomes: On successful completion of this module, students should be able to:
· Describe the rationale for and methods of selecting samples;
· Select appropriate graphics and summary statistics to present and summarise data, with application in Microsoft Excel;
· Interpret graphics and summary statistics to prepare statistical reports;
· Apply probability rules and probability models to solve real problems;
· Apply the theory of sampling distributions to the estimation and hypothesis testing of means and proportions;
· Model the relationships between variables using linear regression analysis;
· Generate and interpret Microsoft Excel analyses of hypothesis tests and linear regression to write statistical reports;
· Design control charts for statistical process control;
· Decompose the components of a time series to forecast future values.

Assessment: Total Marks 200: End of Year Written Examination 140 marks; Continuous Assessment 60 marks (Projects 20 marks; MCQ 40 marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 3 hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 3 hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 5

Teaching Period(s): Teaching Period 1. (Period 1 & 2 for BIS Students).

No. of Students: Min 50, Max 200.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Tutorials; 10 x 1hr(s) Practicals.

Module Co-ordinator: Dr Michael Cronin, Department of Statistics.

Lecturer(s): Dr Michael Cronin, Department of Statistics.

Module Objective: To provide an understanding of the basic methods of business statistics.

Module Content: Data collection and sampling methods. Organising and presenting data. Measures of location and spread. Introduction to probability. Discrete probability distributions, including Bernoulli, Binomial and Poisson. Continuous probability distributions, including Normal and Uniform. Sampling distributions. Statistical estimation in large samples. Statistical analysis and reporting using Microsoft Excel.

Learning Outcomes: On successful completion of this module, students should be able to:
· Describe the rationale for and methods of selecting samples.
· Select appropriate graphics and summary statistics to present and summarise data, with application in Microsoft Excel.
· Interpret graphics and summary statistics to prepare statistical reports.
· Apply probability rules and probability models to solve real problems.
· Calculate and interpret confidence intervals for means and proportions using large samples.

Assessment: Total Marks 100: End of Year Written Examination 70 marks; Continuous Assessment 30 marks (Project 15 marks and MCQ 15 marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Min 5, Max 60.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Practicals (Labs).

Module Co-ordinator: Dr Eric Wolsztynski, Department of Statistics.

Lecturer(s): Dr Eric Wolsztynski, Department of Statistics.

Module Objective: To provide an introduction to Probability and Statistics.

Module Content: Introduction to uncertainty and variability, with examples. Summarization methods for data. Concepts of probability, conditional probability, Bayes' Theorem. Random variables and probability distributions, both discrete and continuous, with applications. Populations, variability, and sampling issues. Introduction to statistical inference, including interval estimation and hypothesis testing. Introduction to statistical modeling.

Learning Outcomes: On successful completion of this module, students should be able to:
· Summarize data distributions using frequency tables and graphs;
· Interpret and choose between alternate measures of centrality and spread;
· Explain, with the use of examples, fundamental concepts of probability;
· Apply probability axioms and rules including Bayes theorem and the law of total probability;
· Describe and apply the concepts of discrete and continuous probability distributions;
· Explain, using examples, alternate sampling techniques;
· Make inferences regarding population parameters based on sample estimates, including the provision of confidence interval estimates, and the testing of statistical hypotheses;
· Describe and apply the simple linear regression model.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks (Homework).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated (as specified by Module Coordinator).

Credit Weighting: 5

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students: Max 350.

Pre-requisite(s): MA1003

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Practicals; 10 x 1hr(s) Tutorials.

Module Co-ordinator: Dr Eric Wolsztynski, Department of Statistics (Department of Statistics).

Lecturer(s): Dr Eric Wolsztynski, Department of Statistics.

Module Objective: To provide an understanding of the applications of statistical methods in the Biological, Environmental, Health and Food Sciences.

Module Content: The application of Statistical Methods in the Biological, Environmental, Health and Food Sciences, with real examples; Descriptive Statistics, Statistical Graphics; Basic Probability concepts; Sampling and Sample Selection methods; Sampling Distributions; Estimation and Hypothesis Testing.

Learning Outcomes: On successful completion of this module, students should be able to:
· Describe and summarise quantitative data;
· Compute marginal, joint and conditional probabilities;
· Describe and apply discrete and continuous distributions;
· Recognise and identify the key elements of estimation and hypothesis testing;
· Compute and interpret confidence intervals for a single mean and for the differences between two means;
· Formulate hypotheses, interpret and derive conclusions from SPSS output for comparing means;
· Determine and model bivariate associations.

Assessment: Total Marks 100: End of Year Written Examination 70 marks; Continuous Assessment 30 marks (2 x MCQ (2 x 5 marks each), 10 x in-tutorial tests (10 x 1 mark each), In-practical SPSS Project (10 marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 5

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students: Max 50.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: Other (24hrs Lectures and Practicals).

Module Co-ordinator: Dr Michael Cronin, Department of Statistics (Department of Statistics).

Lecturer(s): Dr Michael Cronin, Department of Statistics.

Module Objective: To introduce methods of quantitative analysis of particular relevance to social research and the design and analysis of social surveys.

Module Content: Tests for Comparing Means and Proportions; Measures and Tests of Associations; Linear Regression Models; ANOVA Models; Dummy Variables; General Linear Models; Analysis of Covariance; Interactions; Multiple and Partial Correlations; Logistic Regression Models; Model Building and Assessment; Sampling Methods and Statistical Issues in Survey Design.

Learning Outcomes: On successful completion of this module, students should be able to:
· Conduct and interpret hypothesis tests of means, proportions and frequencies;
· Perform and interpret measures and tests of associations;
· Apply and interpret linear regression, ANOVA, general linear models and logistic regression models;
· Interpret dummy variables;
· Test and interpret interactions;
· Understand key concepts in model building including assessing the assumptions of the fitted models;
· Describe sampling methodologies for research studies; Discuss the statistical issues relevant to survey design.

Assessment: Total Marks 100: End of Year Written Examination 60 marks; Continuous Assessment 40 marks (4 assignments, each worth 10 marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 5

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students: Max 30.

Pre-requisite(s): ST1001 or its equivalent

Co-requisite(s): None

Teaching Methods: Other (24hrs Lectures and Practicals.).

Module Co-ordinator: Dr Michael Cronin, Department of Statistics.

Lecturer(s): Staff, Department of Statistics.

Module Objective: To introduce methods of quantitative analysis of particular relevance to social research and the design and analysis of social surveys.

Module Content: Multiple and Partial Correlation; Analysis of cavariance, Dummy Variables and Other Applications of the Linear Model; Log-Linear Models; Casual Analysis and Casual Models; Sampling Methods and Statistical Issues in Survey Design.

Learning Outcomes: On successful completion of this module, students should be able to:
· Interpret hypothesis tests of means, proportions and frequencies;
· Interpret linear models;
· Describe sampling methodologies for research studies;
· Discuss the statistical issues relevant to survey design;
· Interpret logistic regression models;
· Distinguish between linear and logistic regression models.

Assessment: Total Marks 100: End of Year Written Examination 60 marks; Continuous Assessment 40 marks (4 assignments, each worth 10 marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 50%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn.

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Min 5, Max 100.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Tutorials; 10 x 1hr(s) Practicals.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Ms Helena Guiney, School of Mathematical Sciences.

Module Objective: To teach research design methods, the collection, summary analysis and reporting of research data.

Module Content: An introduction to the methods of statistics useful in social research. Use will be made of statistical computing systems.

Learning Outcomes: On successful completion of this module, students should be able to:
· Design a research study to address specific questions.
· Choose sampling methods, interviewing methodologies and questionnaire design to conduct a survey effectively.
· Present and interpret data in graphical format.
· Calculate and interpret appropriate numerical summaries of data.
· Prepare and interpret statistical reports.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks (Assignment (x1) 10 marks, MCQ (x1) 10 marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 10

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students: Min 25.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 48 x 1hr(s) Lectures; 20 x 1hr(s) Tutorials; 20 x 1hr(s) Practicals.

Module Co-ordinator: Dr Michael Cronin, Department of Statistics.

Lecturer(s): Ms Helena Guiney, School of Mathematical Sciences.

Module Objective: To give students a conceptual and practical understanding of the statistical techniques relevant to research in a development context.

Module Content: Study/survey design, sampling methods and data collection, Data processing. Graphical methods of displaying data. Numerical summaries of data. Statistical report writing and interpretation. Sampling distributions. Statistical estimation and hypothesis testing. Applied statistical techniques in a computer package. Simple linear regression analysis. Index Numbers and Time series analysis. Analysis of categorical data.

Learning Outcomes:

Assessment: Total Marks 200: End of Year Written Examination 120 marks; Continuous Assessment 80 marks (Project 40 marks; Assignments 20 marks, MCQ 20 marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 3 hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 3 hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Min 8, Max 100.

Pre-requisite(s): None

Co-requisite(s): ST2011 or equivalent.

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Tutorials; 10 x 1hr(s) Practicals.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Ms Helena Guiney, School of Mathematical Sciences.

Module Objective: To teach statistical methods for testing of hypotheses, assessing relationships and analyzing time series data.

Module Content: Theory of sampling distributions, modeling relationships using simple linear regression, index numbers, time series analysis and categorical data analysis. Use will be made of statistical computing systems.

Learning Outcomes: On successful completion of this module, students should be able to:
· Apply the theory of sampling distributions when estimating and testing hypotheses of means and proportions.
· Apply the theory of simple linear regression analysis to describe relationships between variables.
· Use index numbers to express the relative change in price, quantity, or value from one period to another.
· Use time series analysis to make current decisions and for long-term forecasting and planning.
· Formulate and test hypotheses of association of categorical data.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks (Project 20 marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Max 40.

Pre-requisite(s): ST1051 or permission of lecturer, MA1003 or equivalent

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 12 x 1hr(s) Tutorials.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Staff, Department of Statistics.

Module Objective: To provide a thorough understanding of the concepts of Probability as a basis for further study in Statistics.

Module Content: Review of concepts of probability, conditional probability, Bayes' Theorem, independent events. Random variables, distribution functions, standard discrete and continuous probability distributions, with applications. The Poisson process. Expectations, moments, variance. Chebyshev Inequality, convergence in probability, weak law of large numbers.

Learning Outcomes: On successful completion of this module, students should be able to:
· Explain and apply the concepts of basic probability such as the axioms of probability, counting formulae, conditional probability, Bayes' theorem and independent events;
· Describe and apply the concepts of discrete and continuous random variables and probability distributions, including the standard distributions such as binomial, hypergeometric, Poisson, geometric, negative binomial, uniform, negative exponential, gamma, Erlangian, Normal, beta, Weibull and log-normal;
· Define and apply the concepts of expected value, variance and moments;
· Derive and apply the Chebyshev inequality;
· Define convergence in probability, and prove the weak law of large numbers.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks (Homework).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated (as specified by Module Coordinator).

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Max 40.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 24 x 1hr(s) Practicals.

Module Co-ordinator: Dr Michael Cronin, Department of Statistics.

Lecturer(s): Dr Michael Cronin, Department of Statistics.

Module Objective: To teach the theory and applications of linear statistical models.

Module Content: Review of normal and associated distributions, simple linear regression, multiple regression, drawing conclusions, weighted least squares, regression diagnostics, model building.

Learning Outcomes: On successful completion of this module, students should be able to:
· Describe and apply the theories of simple linear regression.
· Describe and apply the theories of multiple regression.
· Determine when weighted regression is required and to describe and apply the theory.
· Apply regression diagnostics to identify unusual cases.
· Apply regression diagnostics to assess regression assumptions and select remedies when regression assumptions are not valid.
· Compare two or more regression lines using dummy variables.

Assessment: Total Marks 100: Continuous Assessment 100 marks (Assignments 20 marks (5 x 4 marks each); Practical Examination (1 x 80 marks)).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated.

Credit Weighting: 10

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students: Max 60.

Pre-requisite(s): ST1051 or permission of lecturer, MA1003 or equivalent

Co-requisite(s): None

Teaching Methods: 48 x 1hr(s) Lectures; 24 x 1hr(s) Tutorials.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Prof Finbarr O'Sullivan, Department of Statistics.

Module Objective: To provide a thorough understanding of the concepts of Probability and Mathematical Statistics as a basis for study in Actuarial Science and Statistics.

Module Content: Concepts of probability, conditional probability, Bayes' Theorem. Random variables, standard discrete and continuous probability distributions. The Poisson process. Expectations, moments and generating functions. Chebyshev inequality, convergence in probability, weak law of large numbers. Joint, marginal and conditional distributions. Standard multivariate distributions. Distributions of functions of a random variable. Compound distributions. The Central Limit Theorem. Distribution theory of basic statistical methods. Introduction to statistical inference. Constructing estimators: method of moments and maximum likelihood estimation. Interval estimation and hypothesis testing. Comparison of means and one-way analysis of variance

Learning Outcomes: On successful completion of this module, students should be able to:
· Explain and apply the concepts of basic probability, counting formulae, conditional probability, Bayes' theorem and independent events;
· Describe and apply the concepts of discrete and continuous random variables and probability distributions, including the standard distributions such as binomial, hypergeometric, Poisson, geometric, negative binomial, uniform, negative exponential, gamma, Erlangian, Normal, beta, Weibull and log-normal;
· Define and apply the concepts of expectations, moments, cumulants, and generating-functions;
· Derive and apply the Chebyshev inequality; define convergence in probability, and prove the weak law of large numbers;
· Explain and apply the concepts of joint distributions, marginal distributions and conditional distributions, including standard multivariate distributions such as the multinomial and the multivatiate Normal, and the concepts of conditional expected value and conditional variance;
· Describe and apply methods for finding the distribution of a function of a single random variable, and for finding the distribution of a function of several random variables;
· State, prove and apply the Central Limit Theorem. Explain and apply the concepts of random sampling, statistical inference and sampling distribution; derive basic sampling distributions such as that of the sample mean, sample variance, t-statistic and F-ratio, and apply these to the construction of confidence intervals, and to the testing of statistical hypotheses;
· Describe and apply methods for deriving and assessing estimators for parameters of a probability distribution such as the method of moments and the method of maximum likelihood;
· Explain and apply the methods of analysis of variance for the comparison of several population means.

Assessment: Total Marks 200: End of Year Written Examination 160 marks; Continuous Assessment 40 marks (Homework).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 3 hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 3 hr(s) paper(s) to be taken in Autumn. Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated (as specified by Module Coordinator).

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Min 10, Max 200.

Pre-requisite(s): ST1023

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Tutorials; 10 x 1hr(s) Practicals.

Module Co-ordinator: Dr Michael Cronin, Department of Statistics.

Lecturer(s): Dr Michael Cronin, Department of Statistics.

Module Objective: To provide an understanding of further techniques of business statistics.

Module Content: Statistical Estimation in Small Samples. Introduction to Hypothesis testing. Simple linear regression analysis. Introduction to statistical process control. Time series analysis and forecasting. Contingency tables and goodness of fit tests. Statistical analysis and reporting using Microsoft Excel.

Learning Outcomes: On successful completion of this module, students should be able to:
· Apply the theory of sampling distributions to the estimation and hypothesis testing of means and proportions;
· Model the relationships between variables using linear regression analysis;
· Generate and interpret Microsoft Excel analyses of hypothesis tests and linear regression to write statistical reports;
· Design control charts for statistical process control;
· Decompose the components of a time series to forecast future values.

Assessment: Total Marks 100: End of Year Written Examination 70 marks; Continuous Assessment 30 marks.

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 10

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students: Min 12, Max 50.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 8 x 1hr(s) Tutorials; 10 x 1hr(s) Practicals; 6 x 1hr(s) Directed Study.

Module Co-ordinator: Dr Michael Cronin, Department of Statistics.

Lecturer(s): Staff, Department of Statistics.

Module Objective: To provide a basic training in methods for data analysis relevant to Environmental Studies.

Module Content: Presenting and Summarising data, Probability, The Normal Distribution, Estimation, Comparing Averages, Comparing Proportions, Correlation, Regression, Frequency Analysis

Learning Outcomes: On successful completion of this module, students should be able to:
· Describe how to draw random samples;
· Present and interpret data in graphical format and using numerical summaries;
· Assess normality of data;
· Calculate confidence intervals for means and proportions;
· Perform hypothesis tests of means and proportions;
· Apply non-parametric tests to numeric data;
· Model the relationship between two variables using regression analysis;
· Compare proportions and frequencies using the Chi-squared test.

Assessment: Total Marks 200: End of Year Written Examination 140 marks; Continuous Assessment 60 marks (3 x written assignments (12 marks each), 1 computer practical assignment (24 marks)).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Where work is submitted up to and including 7 days late, 5% of the total marks available shall be deducted from the mark achieved. Where work is submitted up to and including 14 days late, 10% of the total marks available shall be deducted from the mark achieved. Work submitted 15 days late or more shall be assigned a mark of zero.

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 3 hr(s) paper(s) to be taken in Winter.

Requirements for Supplemental Examination: 1 x 3 hr(s) paper(s) to be taken in Autumn.

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Min 8, Max 100.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Tutorials; 10 x 1hr(s) Practicals.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Staff, Department of Statistics.

Module Objective: To teach research design methods, the collection, summary analysis and reporting of research data.

Module Content: An introduction to the methods of statistics useful in social research. Use will be made of statistical computing systems.

Learning Outcomes: On successful completion of this module, students should be able to:
· Design a research study to address specific questions.
· Choose sampling methods, interviewing methodologies and questionnaire design to conduct a survey effectively.
· Present and interpret data in graphical format.
· Calculate and interpret appropriate numerical summaries of data.
· Prepare and interpret statistical reports.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks (Assignment (x1) 10 marks, MCQ (x1) 10 marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Min 8, Max 100.

Pre-requisite(s): ST2011 or equivalent.

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Tutorials; 10 x 1hr(s) Practicals.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Ms Helena Guiney, School of Mathematical Sciences.

Module Objective: To teach statistical methods for testing of hypotheses, assessing relationships and analyzing time series data.

Module Content: Theory of sampling distributions, modeling relationships using simple linear regression, index numbers, time series analysis and categorical data analysis. Use will be made of statistical computing systems.

Learning Outcomes: On successful completion of this module, students should be able to:
· Apply the theory of sampling distributions when estimating and testing hypotheses of means and proportions.
· Apply the theory of simple linear regression analysis to describe relationships between variables.
· Use index numbers to express the relative change in price, quantity, or value from one period to another.
· Use time series analysis to make current decisions and for long-term forecasting and planning.
· Formulate and test hypotheses of association of categorical data.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks (Project (x1) 20 marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 10

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students: Max 40.

Pre-requisite(s): ST2054

Co-requisite(s): None

Teaching Methods: 48 x 1hr(s) Lectures; 10 x 1hr(s) Tutorials; 10 x 1hr(s) Practicals.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Dr Supratik Roy, School of Mathematical Sciences.

Module Objective: To develop theoretical foundations for statistical inference.

Module Content: Methods of Inference. Point and Interval Estimation. Efficiency and the Cramer-Rao lower bound. Sufficiency and completeness. Uniformly Minimum Variance Unbiased Estimation. Sampling distributions. Hypothesis Testing and Power of tests. Decision Theory and Bayesian Inference. Application to Risk and Credibility theory, premiums and credibility factors. Game Theory and Inference. Classification and Discrimination.

Learning Outcomes: On successful completion of this module, students should be able to:
· Prove and apply the general results of parametric inference;
· Construct estimators and verify their optimality;
· Construct tests of hypotheses based on established criteria like power;
· Apply the basic principles of Decision theory and Bayesian inference;
· Apply principles of parametric inference to solve concrete problems in Risk Theory;
· Apply principles of parametric inference to solve concrete problems in classification and discrimination;
· Apply a unified framework of statistical inference to Game Theory, GARCH in time series, Neural Networks and Data Mining in Classification, and study of social networks.

Assessment: Total Marks 200: End of Year Written Examination 160 marks; Continuous Assessment 40 marks (homework, project).

Compulsory Elements: End of Year Written Examination; Continuous Assessment. Homework will carry a maximum obtainable mark of 20, and the project will carry a maximum obtainable mark of 20.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 3 hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 3 hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Max 40.

Pre-requisite(s): ST2054

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 12 x 1hr(s) Tutorials.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Prof Finbarr O'Sullivan, Department of Statistics.

Module Objective: To provide an introduction to the theory and applications of stochastic processes.

Module Content: Examples of stochastic processes, random walk, martingales in discrete time, Poisson process, the Chapman-Kolmogorov equations, time-homogenous Markov chains, stationary distribution, Credit Risk Modelling, Simulation, Bayesian Models.

Learning Outcomes: On successful completion of this module, students should be able to:
· Define and recognize categories of stochastic processes with discrete/continuous state spaces arising in discrete/continuous time;
· Define and assess martingale characteristics of discrete time stochastic processes. Apply basic versions of the optional stopping theorem to simple random walks in order to compute ruin probabilities and associated characteristics of simple gaming;
· Define a homogenous the Poisson process. Derive associated properties obtained from addition and thinning of the Poisson process. Develop and apply probabilistic characteristics of holding times;
· Define discrete time discrete/ discrete space Markov chains. Develop the associated Chapman-Kolmogorov equations;
· Show how time-homogeneous Markov chains can be applied to analyze simplified problems arising in insurance, gaming, physical sciences, and health applications;
· Categorize limiting distributional characteristics of Markov chain based on properties of the transition probability matrix or its graphical representation. Be able to apply these results to standard problems;
· Describe and analyze a simple Markov chain formulation of the Jarrow-Lando-Turnbull model of credit risk ;
· Analyse Bayesian Models of Insurance Risk;
· Use Monte-Carlo simulation techniques to quantify risk.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks (Departmental Tests; Assignments).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Max 50.

Pre-requisite(s): ST2053, ST2054,

Co-requisite(s): ST3053

Teaching Methods: 24 x 1hr(s) Lectures; 12 x 1hr(s) Tutorials.

Module Co-ordinator: Dr Eric Wolsztynski, Department of Statistics.

Lecturer(s): Dr Eric Wolsztynski, Department of Statistics.

Module Objective: To develop techniques for the analysis of survival data.

Module Content: Parametric models of survival, use of life tables, types of censoring, hazard functions. Non-parametric estimation of hazard and survival functions, Kaplan-Meier and Nelson-Aalen estimators. Proportional hazards model with covariates. Use of software.

Learning Outcomes: On successful completion of this module, students should be able to:
· Explain the concept of a survival model and be able to describe the more commonly used mortality / survival functions and apply these to solve practical problems.
· Define the distribution and density functions of the random future lifetime, the survival function, the force of mortality and derive relationships between them.
· State the Gompertz and Makeham laws of mortality and be able to apply both to solve practical problems.
· Describe various ways in which lifetime data might be censored and be able to describe the various problems introduced by censoring.
· Describe both the Kaplan-Meier and Nelson-Aalen estimate of the survival function in the presence of censoring, explain how it arises as a maximum likelihood estimate, compute it from typical data and estimate its variance.
· Describe the Cox model for proportional hazards, derive the partial likelihood estimate in the absence of ties, and state its asymptotic distribution.
· Interpret the effect of covariates on the hazard of a population at risk in the Cox proportional hazards model.
· Explain and apply the concept of proportional hazards model selection using likelihood ratio tests.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks (Home Assignments x 2 [10 marks each]).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Max 40.

Pre-requisite(s): ST2053, ST2054

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 12 x 1hr(s) Practicals (Laboratories).

Module Co-ordinator: Dr Michael Cronin, Department of Statistics.

Lecturer(s): Dr Michael Cronin, Department of Statistics.

Module Objective: Explain concepts of a generalized linear model (GLM) and describe how a GLM may be applied to real data.

Module Content: Exponential family distributions (illustrations with Gaussian, binomial, Poisson and Gamma). Mean and variance properties. Specification of GLM models, link functions and linear predictors. Canonical links. Continuous and categorical predictors. Fitting GLM models, the IRLS algorithm. Analysis of deviance, overdispersion and scaling. Inference for model parameters. Model Diagnostics, Pearson and deviance residuals. Goodness of fit. Pearson chi-squared and likelihood ratio tests. Emphasis on applications to Binomial and Poisson response data.

Learning Outcomes: On successful completion of this module, students should be able to:
· Define and apply the fundamental concepts of a generalized linear model (GLM), and describe how a GLM may be applied and interpreted in the analysis of data.
· Define the exponential family of distributions and associated mean and variance properties with particular reference to standard distributions such as the binomial, Poisson, exponential, gamma, and normal.
· Define and apply link functions, including the canonical link, in the context of GLMs
· Describe linear predictors, illustrating their structure for simple models involving continuous and categorical variables. Show how models with offsets can be applied to incorporate stratification patterns into GLM.
· Interpret GLM coefficients for main effects and interactions in models with categorical and continuous covariates, in particular for logistic regression and log-linear models.
· Develop how the parameters of a GLM may be estimated by iterative adaptation of inference techniques from the weighted normal linear model.
· Define the deviance and scaled deviance as well as analogues based on Pearson goodness of fit. Show how to apply and interpret analysis of deviance as a generalization of analysis of variance in normal linear models, with and without adjustment for over-dispersion.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks (Computer practicals x 6 [1 mark each], Homework x 3 [2 marks each], in-class mid-tem [8 marks]).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 5

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students: Min 5, Max 50.

Pre-requisite(s): ST1051 or equivalent, ST2053, or permission of lecturer

Co-requisite(s): ST3053

Teaching Methods: 18 x 1hr(s) Lectures; 18 x 1hr(s) Practicals.

Module Co-ordinator: Dr Eric Wolsztynski, Department of Statistics.

Lecturer(s): Dr Eric Wolsztynski, Department of Statistics.

Module Objective: To use software packages to implement applied statistical techniques.

Module Content: Graphical Data Summaries, Stochastic Modelling, Scenario Testing, Non-parametric statistics, Anova, Simulation Methods, Operational Research Methods, Pivot Tables, Data Frames, Cluster Analysis.

Learning Outcomes: On successful completion of this module, students should be able to:
· Implement statistical tests and analyses in software packages such as R and Excel;
· Present and summarize data in graphical form;
· Handle various data files and formats;
· Use Pivot Tables to summarise large data-sets;
· Build and run a simple stochastic model and interpret the output;
· Apply and interpret an ANOVA;
· Use simulation techniques to describe and represent real-world processes;
· Implement non-parametric techniques to analyse datasets;
· Implement a cluster analysis in a software package.

Assessment: Total Marks 100: Continuous Assessment 100 marks (2 x Practical examination 70 marks; 4 x Assignments 30 marks).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: The mark for Continuous Assessment is carried forward (Assignments), Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated (Students failing Practical Exam must retake it for the supplemental exam, as prescribed by the Department).

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Min 5, Max 30.

Pre-requisite(s): st2054

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 12 x 1hr(s) Tutorials.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Dr Eric Wolsztynski, Department of Statistics.

Module Objective: To teach the theory and applications of time series analysis.

Module Content: Stationary models, autocovariance and autocorrelation, ARIMA processes and Box-Jenkins Modelling, integrated and cointegrated time series.

Learning Outcomes: On successful completion of this module, students should be able to:
· Define and apply the concepts of strong and second order stationary to random series.
· Recognize and be able to empirically apply and adapt transformations, differencing and trend removal (including seasonal adjustment) in order to identify approximate stationarity, I(0), and integrated, I(d), time series.
· Define and explain the concepts and basic properties (including stationarity and invertibility, auto-correlation, partial auto-correlation and the Yule-Walker equations) of autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) time series models.
· Describe and apply to real data the processes of identification, estimation and diagnostic checking for ARIMA time series models. Be able to interpret standard model output.
· Develop, apply and evaluate forecasting techniques based on fitted ARIMA models.
· Define and apply the basic concept of a multivariate autoregressive model and simple examples of of cointegrated time series. Show that certain univariate time series models have the Markov property and describe how to rearrange a univariate time series model as a multivariate Markov model.
· Recognize non-stationary variance patterns and show how simple models, including GARCH, can be applied.
· Carry out a range of analysis techniques using the R statistical computing environment, including removing trends, differencing, identifying and fitting ARIMA models, and forecasting.

Assessment: Total Marks 100: Continuous Assessment 100 marks (computer practicals x 10 [5 marks], Homework x 2 [20 marks]; In-class test x 2 [75 marks]).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated.

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Max 50.

Pre-requisite(s): ST3053

Co-requisite(s): ST3054

Teaching Methods: 24 x 1hr(s) Lectures; 12 x 1hr(s) Tutorials.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Mr Damian Conway, Department of Statistics.

Module Objective: To teach the use of techniques for reporting and analysing life table data.

Module Content: Exposed to risk calculations, methods of graduation and diagnostic checks for graduation; Markov Jump Process and applications thereof. Binomial and Poisson Models of Mortality.

Learning Outcomes: On successful completion of this module, students should be able to:
· Define and apply a Markov Jump Process / General Markov Model and demonstrate how such a process / model can be used as a tool for analyzing various insurance contracts.
· Derive the Kolmogorov equations for a Markov Jump Process / General Markov Model and solve the Kolmogorov equations, in simple cases, to obtain explicit expressions for the key probabilities associated with the process.
· Describe the Binomial and Poisson models of mortality and derive estimates of the rates and forces of mortality for both models.
· Describe how to estimate transition intensities, depending on age, exactly or by using the census approximation.
· Calculate a central exposed to risk given a dataset.
· Explain the concept of a rate interval and develop census formulae given various definitions of age.
· Describe the process of actuarial graduation by a number of methods and state the advantages and disadvantages of each.
· Carry out a comparison of a set of crude estimates and a standard table, or of a set of crude estimates and a set of graduated estimates.
· Describe how to carry out a number of key statistical tests of crude estimates for consistency with a standard table or a set of graduated estimates.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks (1 x In-class Test 12marks, 1 x Homework 8 marks)).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated.

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Min 5, Max 50.

Pre-requisite(s): ST1051 (or equivalent)

Co-requisite(s): none

Teaching Methods: 24 x 1hr(s) Lectures; 12 x 1hr(s) Tutorials.

Module Co-ordinator: Ms Linda Daly, Department of Statistics.

Lecturer(s): Ms Linda Daly, Department of Statistics, Invited Lecturers from the Actuarial Profession.

Module Objective: To provide an introduction to the principles of Financial Reporting and to the theory of Corporate Finance as used by actuaries.

Module Content: Structures of Limited Companies, Methods of Financing, Non-parametric methods, Taxation of life and pension funds, Instruments in use in the Insurance Industry, Institutions in the Insurance Market, Preparation of Statements of Limited Companies, Financial Instruments.

Learning Outcomes: On successful completion of this module, students should be able to:
· Outline the main forms of financial instruments used by companies;
· Understand the methods by which limited companies can raise finance;
· Prepare and present profit and valuation statements typically of a limited company;
· Understand and interpret critically profit and valuation statements of a limited company;
· Describe the role played in markets by a number of risk-based institutions and the instruments they issue;
· Outline ways to identify and quantify risk inherent in capital projects;
· Use non-parametric techniques to critically assess the viability of investment projects and in particular assess the risk underlying such projects;
· Describe the basic principles of personal / investment / corporate taxation;
· Describe the main methods of short and medium term company finance.

Assessment: Total Marks 100: Continuous Assessment 100 marks (2 x in-Class Tests (1 x 10 marks, 1 x 80 marks); 1 x Homework, 10 marks).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: The mark for Continuous Assessment is carried forward, Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated.

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Min 5, Max 50.

Pre-requisite(s): ST1023 or equivalent

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Practicals.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Mr Damian Conway, Department of Statistics.

Module Objective: To provide an introduction to Computational Methods for Data Analysis.

Module Content: Anova and multiple comparisons, Non-parametric and distribution free tests, Monte Carlo simulation, Time Series, Decision Making and Risk Measures.

Learning Outcomes: On successful completion of this module, students should be able to:
· Carry out non-parametric tests using R;
· Compare and contrast non-parametric tests with the equivalent parametric tests;
· Apply and interpret an ANOVA;
· Approximate expectations and p-values using Monte Carlo methods;
· Carry out a Time Series analysis and interpret the results;
· Define, and carry out calculations using, risk measures such as expected (present) values, standard deviation, coefficient of variation etc.;
· Analyse the risk and reward profile of decision problems;
· Use computer packages to perform statistical analyses on datasets and present such analyses in a suitable report.

Assessment: Total Marks 100: Continuous Assessment 100 marks (1 x Practical Examination 75 marks; Assignments 25 marks (3 x 5 marks and 1 x 10 marks)).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: The mark for Continuous Assessment is carried forward (Assignments), Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated (Students failing Practical Exam must retake it for the supplemental exam, as prescribed by the Department).

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Min 10.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Tutorials.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Dr Supratik Roy, School of Mathematical Sciences.

Module Objective: To teach the application of the methods of probability and statistics in engineering and science.

Module Content: The application of the methods of probability and statistics in engineering and science. Examples of non-deterministic phenomena in the physical sciences, descriptive statistics, basic notions of probability, conditional probability and independence, random variables, probability distributions with applications in quality control and reliability, sampling distributions and elementary inference, introduction to common statistical methods.

Learning Outcomes: On successful completion of this module, students should be able to:
· do preliminary exploratory graphical analysis and representation of data, together with applications of statistical techniques for summarization and drawing numerical conclusions;
· be able to able to verify and use the fundamental axioms of probability theory, notions of events, probability space, conditional probability and independence, random variables and their expectations, examples of discrete and continuous probability distributions;
· be able to derive and apply sampling distributions, and use them for estimation, construction of confidence intervals and tests of simple hypothesis;
· have basic competence in applying statistical tools like regression, and be familiar with the basic principles of sampling theory and design of experiments.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks (Homework).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. The mark for Continuous Assessment is carried forward.

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Min 12, Max 50.

Pre-requisite(s): ST2903

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures (Evening); 8 x 1hr(s) Practicals (Evening).

Module Co-ordinator: Dr Michael Cronin, Department of Statistics.

Lecturer(s): Staff, Department of Statistics.

Module Objective: To provide an understanding of quantitative methods and approaches applicable to monitoring and assessing the environment.

Module Content: Environmental Sampling and Design; Environmental Monitoring; Resource Modelling and Prediction; Environmental Risk Assessment and Analysis

Learning Outcomes: On successful completion of this module, students should be able to:
· Apply appropriate Analysis of Variance models using a computer package;
· Design and assess control charts to monitor environmental variables;
· Compare sampling designs and analysis methods for environmental impact assessments;
· Apply equivalence tests to determine environmental reclamation;
· Describe the principles and methods of Monte-Carlo simulation of environmental risk assessment;
· Compare methods of dealing with censored data.

Assessment: Total Marks 100: End of Year Written Examination 70 marks; Continuous Assessment 30 marks (2 x written assignments (8 marks each), 1 x computer practical assignment (14 marks)).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Where work is submitted up to and including 7 days late, 5% of the total marks available shall be deducted from the mark achieved. Where work is submitted up to and including 14 days late, 10% of the total marks available shall be deducted from the mark achieved. Work submitted 15 days late or more shall be assigned a mark of zero.

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn.

Credit Weighting: 10

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students: Max 20.

Pre-requisite(s): ST2053, ST2054

Co-requisite(s): None

Teaching Methods: 48 x 1hr(s) Lectures; 24 x 1hr(s) Tutorials.

Module Co-ordinator: Ms Kathleen (Catherine) O'Sullivan, School of Mathematical Sciences (Department of Statistics).

Lecturer(s): Ms Kathleen (Catherine) O'Sullivan, School of Mathematical Sciences.

Module Objective: To provide training for work as a statistical consultant.

Module Content: Basic guidelines for consulting. Development of goals. Experience in consulting will be developed in an apprenticeship format with practical consulting for short-term and long-term clients.

Learning Outcomes: On successful completion of this module, students should be able to:
· Conduct and interpret appropriate statistical analyses of data;
· Assess critical assumptions associated with these methods;
· Employ problem solving skills in order to address statistical issues;
· Perform statistical analysis using industrial standard software (SAS, SPSS);
· Evaluate the statistical methodologies employed in published material from a variety of disciplines;
· Work effectively as an individual, in teams and as part of a multi-disciplinary team;
· Apply and master transferable skills such as data management, data analysis, problem solving, statistical computing, oral and written communication (both in technical and non technical manners), report writing and presentation delivery.

Assessment: Total Marks 200: Continuous Assessment 200 marks (Consulting Portfolio 1, 40 marks; Poster, 20 marks; Poster Oral Presentaion, 20 marks; Consulting Portfolio 2, 80 marks; Oral Presentation, 20 marks; Reflective Report, 20 marks).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40% In addition students must achieve at least 40% in each element of Continuous Assessment.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated.

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Min 5, Max 30.

Pre-requisite(s): ST2054

Co-requisite(s): ST3054

Teaching Methods: 24 x 1hr(s) Lectures; 12 x 1hr(s) Tutorials.

Module Co-ordinator: Ms Linda Daly, Department of Statistics.

Lecturer(s): Staff, Department of Statistics.

Module Objective: To provide an understanding of the theory and methods of life insurance and pension fund mathematics, together with relevant applications.

Module Content: Select and ultimate life tables. Analysis of annuity and assurance contracts. Evaluation of assurances and annuities. Actuarial funding. Calculation of provisions by methods of net and gross premiums. With-profits policies.

Learning Outcomes: On successful completion of this module, students should be able to:
· Define simple assurance and annuity contracts, and develop formulae for the means and variances of the present values of the payments under these contracts, assuming constant deterministic interest.
· Be able to use standard Actuarial Tables for evaluation of expected present values of simple insurance contracts including: whole life assurance, term assurance, pure endowment and endowment assurance.
· Apply practical methods of evaluating expected values and variances of the contracts in (1) using standard Actuarial Tables.
· Describe and calculate, using ultimate or select mortality, net premiums and net premium reserves of simple insurance contracts listed in (1).
· Describe the calculation, using ultimate or select mortality, of net premiums and net premium reserves for increasing and decreasing benefits and annuities.

Assessment: Total Marks 100: Continuous Assessment 100 marks (2 x In-Class Test (1 x 10 marks) and (1 x 80marks); Homeworks (1 x 10 marks)).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated.

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Min 5, Max 30.

Pre-requisite(s): ST2054, ST4066.

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 12 x 1hr(s) Tutorials.

Module Co-ordinator: Ms Linda Daly, Department of Statistics.

Lecturer(s): Staff, Department of Statistics.

Module Objective: To provide an understanding of the theory and methods of life insurance and pension fund mathematics, together with relevant applications.

Module Content: Mortality data. Competing risks. Multiple decrement tables. Disability and long-term care contracts. Assurance and annuities for two lives. Contingent and reversionary benefits. Profit testing. Pension fund mathematics. Heterogeneity of populations and population projections.

Learning Outcomes: On successful completion of this module, students should be able to:
· Define simple assurance and annuity contracts involving two lives, and develop formulae for the means and variances of the present values of the payments under these contracts, assuming constant deterministic interest.
· Describe the calculation of gross premiums and reserves of assurance and annuity contracts.
· Describe the technique of discounted emerging costs, for use in pricing, reserving, and assessing profitability and be able to carry out simple profit testing to be used to price an insurance product or to determine reserves.
· Describe methods which can be used to model cashflows contingent upon competing risks and in particular explain how the value of a cashflow, contingent upon more than one risk, may be valued using a multiple-state Markov Model.
· State the principal factors which contribute to the variation in mortality and morbidity by region and according to the social and economic environment.
· Define and give examples of the main forms of selection and explain how selection can be expected to occur amongst individuals taking out each of the main types of life insurance contracts, or amongst members of large pension schemes.

Assessment: Total Marks 100: Continuous Assessment 100 marks (1 x 10 mark homework, 1 x 10 mark class test, 1 x 80 mark lab based exam).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated.

Credit Weighting: 10

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students: Min 5, Max 30.

Pre-requisite(s): ST2054

Co-requisite(s): ST3054

Teaching Methods: 48 x 1hr(s) Lectures; 20 x 1hr(s) Tutorials.

Module Co-ordinator: Ms Linda Daly, Department of Statistics.

Lecturer(s): Ms Linda Daly, Department of Statistics, Invited lecturers from the Actuarial Profession.

Module Objective: To provide an understanding of the theory and methods of single and joint life insurance and pension fund mathematics, together with relevant applications.

Module Content: Select and ultimate life tables. Analysis of single and joint annuity and assurance contracts. Evaluation of single and joint assurances and annuities. Calculation of provisions by methods of net and gross premiums. With-profits policies. Competing risks. Multiple decrement tables. Contingent and reversionary benefits. Profit testing. Pension fund mathematics. Mortality Statistics. Selection.

Learning Outcomes: On successful completion of this module, students should be able to:
· Define simple assurance and annuity contracts for single and joint life, and develop formulae for the means and variances of the present values of the payments under these contracts, assuming constant deterministic interest.
· Be able to use Standard Actuarial Tables for evaluation of expected present values of simple insurance contracts incuding: whole life assurance, term assurance, pure endowment and endowment assurance.
· Apply practical methods of evaluating expected values and variances of insurance contracts using standard Actuarial Tables for both single and joint life contracts.
· Describe and calculate, using ultimate or select mortality, net premiums and net premium reserves of insurance contracts.
· Describe the calculation, using ultimate or select mortality, of net premiums and net premium reserves for increasing and decreasing benefits and annuities.
· Describe the technique of discounted emerging costs, for use in pricing, reserving, and assessing profitability and be able to carry out simple profit testing to be used to price an insurance product or to determine reserves.
· Describe methods which can be used to model cashflows contingent upon competing risks and in particular explain how the value of a cashflow, contingent upon more than one risk, may be valued using a multiple-state Markov Model.
· Define and give examples of the main forms of selection and explain how selection can be expected to occur amongst individuals taking out each of the main types of life insurance contracts, or amongst members of large pension schemes.

Assessment: Total Marks 200: Continuous Assessment 200 marks (2 In Class Tests (1 x 20 marks) and (1 x 160 marks); Homeworks (2 x 10 marks)).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated.

Credit Weighting: 5

Teaching Period(s): Teaching Periods 1 and 2.

No. of Students:

Pre-requisite(s): ST1051 or equivalent

Co-requisite(s): None

Teaching Methods: Directed Study (Directed Reading; Individual Research; Computer Analysis; Presentation of Findings).

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Dr Supratik Roy, School of Mathematical Sciences.

Module Objective: To develop independent research, presentation and communication skills.

Module Content: Research related to Statistics / Applied Statistics.

Learning Outcomes: On successful completion of this module, students should be able to:
· Describe a methodological area of topical interest in Statistics;

· Present key theoretical results that underpin the use of the methodology;
· Apply the methodology in a practical setting.

Assessment: Total Marks 100: Continuous Assessment 100 marks (Report 90 marks; Departmental Tests and Assignments 10 marks).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated (Resubmission of revised Project Report and presentation of findings, as prescribed by the Department).

Credit Weighting: 30

Teaching Period(s): Teaching Period 2.

No. of Students: Min 1, Max 10.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 72 x 1hr(s) Tutorials; 4 x 1month(s) Directed Study (Directed Reading; Individual Research; Computer Analysis; Presentation of Findings).

Module Co-ordinator: Dr Jian Huang, Department of Statistics.

Lecturer(s): Dr Jian Huang, Department of Statistics.

Module Objective: To develop independent research skills

Module Content: The project requires students to research an area of interest in statistics, plan and execute a
programme of investigative statistical data analysis work, write a report and present the work in the form of a seminar to the class.

Learning Outcomes: On successful completion of this module, students should be able to:
· Undertake a research project by independent research;
· Acquire relevant material from the scientific literature;
· Write a coherent report to describe the background, the methodologies employed and the conclusions of the research study;
· Present the findings of the project to a specialist audience.

Assessment: Total Marks 600: Continuous Assessment 600 marks (i.e report to be submitted by the end of April following the date of first registration).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: No Supplemental Examination.

Credit Weighting: 10

Teaching Period(s): Teaching Period 1.

No. of Students: Min 15, Max 150.

Pre-requisite(s): ST2001 or equivalent

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Tutorials; 10 x 1hr(s) Practicals; 6 x 1hr(s) Directed Study.

Module Co-ordinator: Dr Michael Cronin, Department of Statistics.

Lecturer(s): Mr Cathal Doherty, School of Mathematical Sciences.

Module Objective: To provide an understanding of biostatistical methods applicable to the design of experiments and the reporting and presentation of data analyses.

Module Content: Review of Hypothesis Testing; Frequency Analysis; Parametric and Non-Parametric Regression Analysis; Analysis of Variance; Non-Parametric Techniques; Experimental Design; Presentation of Data Analyses.

Learning Outcomes: On successful completion of this module, students should be able to:
· Use frequency analysis methods to test for randomness, association and goodness of fit;
· Model the relationship between a dependent (response) variable and independent (explanatory) variable using regression analysis;
· Test for differences between means of three or more groups using analysis of variance and apply post hoc tests where appropriate;
· Use suitable non-parametric techniques to test for differences between groups which do not meet the criteria for parametric testing;
· Use the principles of experimental design to plan experiments.

Assessment: Total Marks 200: End of Year Written Examination 120 marks; Continuous Assessment 80 marks (Assignments (4 x 20 marks each)).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 3 hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 3 hr(s) paper(s) to be taken in Autumn. Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated (as specified by Module Coordinator).

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Min 5, Max 150.

Pre-requisite(s): ST1023 or equivalent

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Practicals.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Dr Michael Cronin, Department of Statistics.

Module Objective: To study advanced Data Analysis Methods including Reporting of Results.

Module Content: Review of normal and associated distributions, simple linear regression, multiple regression, drawing conclusions, weighted least squares, regression diagnostics, model building.

Learning Outcomes: On successful completion of this module, students should be able to:
· Describe and apply the theories of simple linear regression;
· Describe and apply the theories of multiple regression;
· Determine when weighted regression is required and to describe and apply the theory;
· Apply regression diagnostics to identify unusual cases;
· Apply regression diagnostics to assess regression assumptions and select remedies when regression assumptions are not valid;
· Compare two or more regression lines using dummy variables.

Assessment: Total Marks 100: Continuous Assessment 100 marks (Assignments 20 marks (5 x 4 marks each); Practical Examination (1 x 80 marks)).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated.

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Min 5, Max 100.

Pre-requisite(s): Introductory course in Probability and Statistics, such as ST1023 or ST2036

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 6 x 1hr(s) Tutorials; 6 x 1hr(s) Practicals.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Mr David Hawe, School of Mathematical Sciences.

Module Objective: To develop an understanding of the application of quantitative and computing methods to decision-making problems in management.

Module Content: Linear programming models for resource allocation; graphical and simplex method of solution; sensitivity analysis and duality; multiple management objectives using goal programming; network flow models for transportation, job-scheduling and inventory management; Integer linear programming; Project-management, network-representations and critical-path analysis; resource-levelling and time-cost tradeoffs.

Learning Outcomes: On successful completion of this module, students should be able to:
· describe the form of a linear programming model for a management decision problem;
· develop linear programming formulations of such problems, and apply the Simplex method for solving linear programming problems, together with associated sensitivity analysis;
· describe the dual of a linear programming problem, and interpret the dual problem in economic terms;
· carry out the Branch and Bound method of solving integer linear programming problems;
· describe the linear programming formulation of the transportation problem, and carry out the solution of such problems, together with associated sensitivity analysis;
· describe and apply the methods of project management, and in particular critical path analysis, to the problem of scheduling the activities of a project;
· formulate and analyse management optimization problems with multiple objectives as Goal programming problems;
· formulate and solve linear progamming problems in a statistical software package.

Assessment: Total Marks 100: Continuous Assessment 100 marks (2 x In-class tests (1 x 10 marks, 1x 80 marks); 1 x Homework 10 marks).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Where work is submitted up to and including 7 days late, 10% of the total marks available shall be deducted from the mark achieved. Where work is submitted up to and including 14 days late, 20% of the total marks available shall be deducted from the mark achieved. Work submitted 15 days late or more shall be assigned a mark of zero.

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated.

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Min 5, Max 150.

Pre-requisite(s): ST4401

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 7 x 1hr(s) Tutorials; 7 x 1hr(s) Practicals.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Ms Kathleen (Catherine) O'Sullivan, School of Mathematical Sciences.

Module Objective: To develop skills in the use of modelling and systems analysis to support decision-making.

Module Content: The structure of decision-making. Sequential Decisions and Decision-tree analysis. The Valuation of Information. Attitudes to Risk. Preference Theory. Simulation Modelling. Simulation Experiments and Interpretation of Simulation Results. Modelling in Operations Management.

Learning Outcomes: On successful completion of this module, students should be able to:
· Describe the structure of decision-making problems that involve uncertain outcomes, including consideration of various decision-making criteria;
· Prepare decision-tree diagrams to represent sequential decision problems, and carry out Decison Tree Analysis, including the evaluation of expected monetary values;
· Describe and apply the elements of Preference Theory to Decision Tree Analysis;
· Use methods of simulation modeling to decision problems that incorporate uncertainty;
· Apply methods for the analysis of inventory management problems, both deterministic and stochastic.

Assessment: Total Marks 100: Continuous Assessment 100 marks (1 x in- class test).

Compulsory Elements: Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): None.

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated.

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Min 5, Max 100.

Pre-requisite(s): Introductory course in Probability and Statistics, such as ST1023 or ST2036

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 6 x 1hr(s) Tutorials; 6 x 1hr(s) Practicals.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Mr David Hawe, School of Mathematical Sciences.

Module Objective: To develop an understanding of the application of quantitative and computing methods to decision-making problems in management.

Module Content: Linear programming models for resource allocation; graphical and simplex method of solution; sensitivity analysis and duality; multiple management objectives using goal programming; network flow models for transportation, job-scheduling and inventory management; Integer linear programming; Project-management, network-representations and critical-path analysis; resource-levelling and time-cost tradeoffs.

Learning Outcomes: On successful completion of this module, students should be able to:
· describe the form of a linear programming model for a management decision problem;
· develop linear programming formulations of such problems, and apply the Simplex method for solving linear programming problems, together with associated sensitivity analysis;
· describe the dual of a linear programming problem, and interpret the dual problem in economic terms;
· carry out the Branch and Bound method of solving integer linear programming problems;
· describe the linear programming formulation of the transportation problem, and carry out the solution of such problems, together with associated sensitivity analysis;
· describe and apply the methods of project management, and in particular critical path analysis, to the problem of scheduling the activities of a project;
· formulate and analyse management optimization problems with multiple objectives as Goal programming problems;
· formulate and solve linear programming problems in a statistical software package.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks (1 x In-class Test 10 marks, 1 x Homework 10 marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Where work is submitted up to and including 7 days late, 10% of the total marks available shall be deducted from the mark achieved. Where work is submitted up to and including 14 days late, 20% of the total marks available shall be deducted from the mark achieved. Work submitted 15 days late or more shall be assigned a mark of zero.

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s) to be taken in Spring.

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn.

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Min 5, Max 150.

Pre-requisite(s): ST4401

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 7 x 1hr(s) Tutorials; 7 x 1hr(s) Practicals.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Ms Kathleen (Catherine) O'Sullivan, School of Mathematical Sciences.

Module Objective: To develop skills in the use of modelling and systems analysis to support decision-making.

Module Content: The structure of decision-making. Sequential Decisions and Decision-tree analysis. The Valuation of Information. Attitudes to Risk. Preference Theory. Simulation Modelling. Simulation Experiments and Interpretation of Simulation Results. Modelling in Operations Management.

Learning Outcomes: On successful completion of this module, students should be able to:
· Describe the structure of decision-making problems that involve uncertain outcomes, including consideration of various decision-making criteria;
· Prepare decision-tree diagrams to represent sequential decision problems, and carry out Decision Tree Analysis, including the evaluation of expected monetary values;
· Describe and apply the elements of Preference Theory to Decision Tree Analysis;
· Describe and apply the methods of simulation modeling to decision problems that incorporate uncertainty;
· Describe and apply methods for the analysis of inventory management problems, both deterministic and stochastic.

Assessment: Total Marks 100: End of Year Written Examination 100 marks.

Compulsory Elements: End of Year Written Examination.

Penalties (for late submission of Course/Project Work etc.): None.

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s) to be taken in Spring.

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn.

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Max 40.

Pre-requisite(s): ST1051 or permission of lecturer or equivalent, MA1003 or equivalent

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 12 x 1hr(s) Tutorials.

Module Co-ordinator: Prof Finbarr O'Sullivan, Department of Statistics.

Lecturer(s): Staff, Department of Statistics.

Module Objective: To provide a thorough understanding of the concepts of Probability as a basis for further study in Statistics.

Module Content: Review of concepts of probability, conditional probability, Bayes' Theorem, independent events. Random variables, distribution functions, standard discrete and continuous probability distributions, with applications. The Poisson process. Expectations, moments, variance. Chebyshev Inequality, convergence in probability, weak law of large numbers. The students will perform self-directed literature/library assignment. This literature/library assignment is designed to teach students how key discoveries were made, the people behind such discoveries and the scientific landscape at the time the ground breaking research was carried out. The literature project will involve independent research from the available literature in the Boole Library, the department and other literature sources. Students will be encouraged to present their own ideas and interpretations of the literature reviewed and to draw conclusions.

Learning Outcomes: On successful completion of this module, students should be able to:
· Explain and apply the concepts of basic probability such as the axioms of probability, counting formulae, conditional probability, Bayes' theorem and independent events;
· Describe and apply the concepts of discrete and continuous random variables and probability distributions, including the standard distributions such as binomial, hypergeometric, Poisson, geometric, negative binomial, uniform, negative exponential, gamma, Erlangian, Normal, beta, Weibull and log-normal;
· Define and apply the concepts of expected value, variance and moments;
· Derive and apply the Chebyshev inequality;
· Define convergence in probability, and prove the weak law of large numbers.
· Interpret, synthesise, and critically assess the current scientific literature on the topic of this module in a literature review format. Students will learn how key discoveries are made in science and the thinking at the time that enabled these discoveries to be made.

Assessment: Total Marks 100: End of Year Written Examination 50 marks; Continuous Assessment 50 marks (Homework 20marks, Literature review 30marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated (as specified by Module Coordinator).

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Min 5, Max 60.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; 10 x 1hr(s) Practicals (Labs).

Module Co-ordinator: Dr Eric Wolsztynski, Department of Statistics.

Lecturer(s): Staff, Department of Statistics.

Module Objective: To provide an introduction to Probability and Statistics.

Module Content: Introduction to uncertainty and variability, with examples. Summarization methods for data. Concepts of probability, conditional probability, Bayes' Theorem. Random variables and probability distributions, both discrete and continuous, with applications. Populations, variability, and sampling issues. Introduction to statistical inference, including interval estimation and hypothesis testing. Introduction to statistical modeling. The students will perform self-directed literature/library assignment. This literature/library assignment is designed to teach students how key discoveries were made, the people behind such discoveries and the scientific landscape at the time the ground breaking research was carried out. The literature project will involve independent research from the available literature in the Boole Library, the department and other literature sources. Students will be encouraged to present their own ideas and interpretations of the literature reviewed and to draw conclusions.

Learning Outcomes: On successful completion of this module, students should be able to:
· Summarize data distributions using frequency tables and graphs;
· Interpret and choose between alternate measures of centrality and spread;
· Explain, with the use of examples, fundamental concepts of probability;
· Apply probability axioms and rules including Bayes theorem and the law of total probability;
· Describe and apply the concepts of discrete and continuous probability distributions;
· Explain, using examples, alternate sampling techniques;
· Make inferences regarding population parameters based on sample estimates, including the provision of confidence interval estimates, and the testing of statistical hypotheses;
· Describe and apply the simple linear regression model.
· Interpret, synthesise, and critically assess the current scientific literature on the topic of this module in a literature review format. Students will learn how key discoveries are made in science and the thinking at the time that enabled these discoveries to be made.

Assessment: Total Marks 100: End of Year Written Examination 50 marks; Continuous Assessment 50 marks (Homework 20 marks, Literature review 30 marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated (as specified by Module Coordinator).

Credit Weighting: 5

Teaching Period(s): Teaching Period 1.

No. of Students: Min 5, Max 20.

Pre-requisite(s): None

Co-requisite(s): MF2052, ST2051

Teaching Methods: 24 x 1hr(s) Lectures; 12 x 1hr(s) Tutorials.

Module Co-ordinator: Mr Damian Conway, Department of Statistics.

Lecturer(s): Staff, Department of Statistics.

Module Objective: Discuss and analyze the different forms of risk and uncertainty in the insurance and financial industries and explain how risk may be modeled and managed mathematically and statistically.

Module Content: Risk Modelling, Risk Aggregation, Credit Risk, Market Risk, Insurance Risk, Risk Management Control Cycle

Learning Outcomes: On successful completion of this module, students should be able to:
· Show an understanding of the different risk categories and explain how risk events may be categorised in different ways.
· Discuss how amenable the various risk categories are to quantitative analysis.
· Define risk measures and describe their properties and limitations.
· Understand how scenario analysis and stress testing is used in the risk modelling process.
· Discuss ways to take account of model and parameter risk.
· Demonstrate how Extreme Value Theory can be used to model risks with very low probabilities.
· Define and evaluate credit and market risk and the modelling thereof.
· Understand risk aggregation, correlation and diversifation.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks ((2 x Homework 10 marks)).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated (as specified by the Module Coordinator).

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Min 5, Max 20.

Pre-requisite(s): None

Co-requisite(s): MF2052, ST2051

Teaching Methods: 24 x 1hr(s) Lectures; 12 x 1hr(s) Tutorials.

Module Co-ordinator: Mr Damian Conway, Department of Statistics.

Lecturer(s): Staff, Department of Statistics.

Module Objective: Discuss the key principles underlying the application of Enterprise Risk Management within a financial or insurance-based organization.

Module Content: Enterprise Risk Management; Financial Derivatives; Risk Modelling; Statistical Analysis; Optimization Techniques, Asset Liability Modelling

Learning Outcomes: On successful completion of this module, students should be able to:
· Understand the principal terms in Enterprise Risk Management (ERM).
· Develop an appropriate framework for an organisation?s ERM.
· Describe the use of models in the ERM decision making process.
· Understand the relevant regulatory frameworks such as Basel 2 and Solvency 2.
· Analyse current and historical case studies of how situations could benefit from ERM
· Recommend approaches that can be used to manage an originations overall risk profile in relation to its risk appetite.
· Investigate a number of risk mitigation activities such as risk transfer, use of insurance and use of financial derivatives
· Describe how to optimise an organization?s risk objective function subject to cost and logical constraints of that organisations
· Analyse financial and insurance date using statistical methods.

Assessment: Total Marks 100: End of Year Written Examination 80 marks; Continuous Assessment 20 marks (2 x Homework 10 marks).

Compulsory Elements: End of Year Written Examination; Continuous Assessment.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: 1 x 1½ hr(s) paper(s).

Requirements for Supplemental Examination: 1 x 1½ hr(s) paper(s) to be taken in Autumn. Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated (as specified by the Module Coordinator).

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Max 35.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures; Other (directed learning).

Module Co-ordinator: Dr Tony Fitzgerald, Department of Epidemiology and Public Health (and Department of Statistics).

Lecturer(s): Staff, School of Mathematical Sciences.

Module Objective: To develop core skills in this key foundation of public health practice

Module Content: Application of biostatistics in public health theory and practice; simple and multiple linear regression; simple and multiple logistic regression; presentation of statistical analysis; Statistical inference; Public Health Informatics

Learning Outcomes: On successful completion of this module, students should be able to:
· Describe the role of biostatistics in the discipline of public health and and describe basic statistical methods routinely employed in health research.
· Demonstrate an understanding of simple and multiple linear and logistic regression models.
· Apply basic informatics techniques to vital statistics and public health records in
· the description of public health characteristics and in public health research and evaluation.
· Interpret results of statistical analyses found in public health studies.
· Develop written and oral presentations based on a detailed statistical analyses for both public health professionals and educated lay audiences.

Assessment: Total Marks 100: Continuous Assessment 100 marks (Project Report: 50 marks; Presentation:50 marks).

Compulsory Elements: Continuous Assessment. (Both components to be passed).

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated (as prescribed by the department).

Credit Weighting: 5

Teaching Period(s): Teaching Period 2.

No. of Students: Max 35.

Pre-requisite(s): None

Co-requisite(s): None

Teaching Methods: 24 x 1hr(s) Lectures (tutorials; self-directed learning).

Module Co-ordinator: Dr Tony Fitzgerald, Department of Epidemiology and Public Health (and Department of Statistics).

Lecturer(s): Staff, School of Mathematical Sciences.

Module Objective: To develop techniques for the analysis of survival data.

Module Content: Survival analysis for the modelling of cohort data. Types of censoring. Kaplan-Meier estimator. Calculating and interpreting relative risks calculated using the proportional hazards model. Model building. Use of software

Learning Outcomes: On successful completion of this module, students should be able to:
· Explain the concept of a survival model and describe the most commonly used mortality / survival functions.
· Explain the ways in which survival data might be censored and describe the problems introduced by censoring.
· Describe the Kaplan-Meier estimate of the survival function in the presence of censoring
· Describe the Cox proportional hazards model
· Apply the Cox proportional hazard model and communicate the results to non-statistical experts.

Assessment: Total Marks 100: Continuous Assessment 100 marks (Project Report: 50 marks; Presentation:50 marks).

Compulsory Elements: Continuous Assessment. (Both components to be passed).

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: 40%.

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated (as prescribed by the department).

Credit Weighting: 10

Teaching Period(s): Teaching Period 2 and Teaching/Research Period 3.

No. of Students: Max 60.

Pre-requisite(s): Students must be registered for a Research Masters or PhD programme in UCC.

Co-requisite(s): None

Teaching Methods: 48 x 1hr(s) Lectures; 24 x 1hr(s) Practicals.

Module Co-ordinator: Ms Kathleen (Catherine) O'Sullivan, School of Mathematical Sciences.

Lecturer(s): Staff, Department of Statistics.

Module Objective: To provide an introduction to the statistical methods relevant to data analysis and practical applications of these methods.

Module Content: Descriptive Statistics; Discrete and Continuous Distributions; Estimation; Hypothesis Testing; Tests of Proportions; Tests of Means; ANOVA Models; Correlation; Linear Regression; Multi-Factor Experiments and Blocking; Residual Diagnostics; Transformations; Study Design; Applications; Implementation of Methods in Statistical Software Packages (e.g. SAS, SPSS).

Learning Outcomes: On successful completion of this module, students should be able to:
· Describe sampling methodologies for research studies;
· Discuss the statistical issues relevant to study design;
· Evaluate the statistical methodologies employed in research papers in their own area;
· Conduct and interpret appropriate statistical analyses of data;
· Assess critical assumptions associated with these methods;
· Perform statistical analysis using industrial standard software (SAS, SPSS).

Assessment: Group Reflective paper, Group presentation, In-class test, Data analysis project, each of which is assessed on a Pass/Fail basis.

Compulsory Elements: Group Reflective Paper; Group Presentation; In-class test; Data Analysis Project.

Penalties (for late submission of Course/Project Work etc.): Work which is submitted late shall be assigned a mark of zero (or a Fail Judgement in the case of Pass/Fail modules).

Pass Standard and any Special Requirements for Passing Module: A Pass/Fail judgement. In addition, students must achieve a Pass Judgement in each element of continuous assessment (Group reflective paper, Group presentation, In-class test, Data analysis project).

End of Year Written Examination Profile: No End of Year Written Examination.

Requirements for Supplemental Examination: Marks in passed element(s) of Continuous Assessment are carried forward, Failed element(s) of Continuous Assessment must be repeated.


		Book of Modules 2012/2013
		STXXXX

Book of Modules 2012/2013

STXXXX