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Postgraduate Certificate in StatisticsCourse Structure

If you are interested in applying to study on this programme through the Higher Education Authority's recently announced Stimulus Programme then please go to the separate FAQ that explains how to apply and how the programme will fit in to this year.

Course Philosophy

The emphasis is on statistical thinking rather than mathematical techniques, consequently statistical or mathematical theory are not discussed. The conceptual basis of the methods is emphasised; the aim is to develop an intuitive understanding of how the methods work. Underlying assumptions of the standard methods and what can be done when these assumptions are invalid are discussed. While it is likely that most participants will have some previous exposure to statistics as undergraduates, the course does not assume prior knowledge of statistical ideas and methods. However, because all participants are graduates, the coverage is conceptually more sophisticated than most undergraduate first level courses.

For Whom is the Course Intended?

The course is intended for graduates of disciplines, other than statistics, who want to develop and deepen their knowledge of statistical methods for solving problems involving data arising in business and industry, in public service agencies or in research agencies. Applications will be considered from degree level graduates in any discipline. While the mathematical level of the course is kept to a minimum, some background in mathematics is essential; Leaving Certificate mathematics is an acceptable standard for most modules.

Many people taking research degrees in other disciplines in Trinity College take the Postgraduate Certificate as a means of developing their research methods skills. This is encouraged by the College and in such cases the tuition fees for the Postgraduate Certificate are waived. Note, though, that students need to register separately for the Postgraduate Certificate - registration for the research degree is not sufficient.

Students taking taught postgraduate courses are NOT NORMALLY GIVEN PERMISSION to take the Postgraduate Certificate in parallel. Students who wish to do so need to apply to the Dean of Graduate Studies for permission. In doing so they should provide a letter/email indicating the support of their Course Director for their request. SUCH PERMISSION IS ONLY GRANTED EXCEPTIONALLY.

The Postgraduate Certificate is designed to be a challenging course for graduates of disciplines other than Statistics.  The great majority of participants will have studied some Statistics at undergraduate level, but this will often have been taught in a cookbook fashion by non-statisticians.  The course aims to develop and enhance the data analytic skills of non-statistical graduates by teaching in a unified and coherent way the inferential ideas and methods of Applied Statistics.  It is not designed for Statistics graduates.  Neither is it an entry point for postgraduate study in Statistical Science and it does not lead on to a Masters level degree in the discipline of Statistics.


All students take the Base Module, which is normally taught on Mondays and Wednesdays during the first 12 week semester, before Christmas.  Students must then take two modules to complete the course. For the first six weeks of the second semester (after Christmas) there will be a module entitled Introduction to Regression (Tuesdays and Thursdays). This will be followed by a six-week module on the Design and Analysis of Experiments (also Tuesdays and Thursdays).

Due to the migration of this programme to be fully online, the module names and structures will change from September 2021. We will list the new modules here once they are ready.  However, the content of the modules will be broadly the same as that given below

ST7001: Base Module

Michaelmas Term (Semester 1): Online through Blackboard
Lecturer: Mimi Zhang Topics covered:

  • Data summaries and graphs
  • Statistical models
  • Sampling distributions: confidence intervals and tests
  • Comparative experiments: t-tests, confidence intervals, design issues
  • Counted data: confidence intervals and tests for proportions, design issues
  • Cross-classified frequency data: chi-square tests
  • Introduction to Regression Analysis
  • Introduction to Analysis of Variance
  • Statistical computing laboratory

Module Description ST7001 Blackboard

ST7002: Introduction to Regression

Hilary Term (Semester 2) Weeks 1-6: Monday and Wednesday (18.00-20.00)
Lecturer: TBA
Topics covered:

  • Statistical versus deterministic relationships
  • Simple linear regression model: assumptions, model fitting, estimation of coefficients and their standard errors
  • Confidence intervals and statistical significance tests on model parameters
  • Prediction intervals
  • Analysis of variance in regression: F-tests, r-squared
  • Model validation: residuals, residual plots, normal plots, diagnostics
  • Multiple regression analysis - short introduction
  • Statistical computing laboratories

Module Description ST7002 Blackboard

ST7003: Design and Analysis of Experiments

Hilary Term (Semester 2): Weeks 7-12: Tuesday and Thursday (18.00 - 20.00)
Lecturer: TBA

This module is concerned with the design of data collection exercises for the assessment of the effects of changes in factors associated with a process and the analysis of the data subsequently produced.

In order to assure that the experimental changes caused the observed effects, strict conditions of control of the process must be adhered to. Specifically, the conditions under which the experimentation is conducted must be as homogeneous as possible with regard to all extraneous factors that might affect the process, other than the experimental factors that are deliberately varied.

The simplest experiments involve comparison of process results when a single factor is varied over two possible conditions.  When more than two factors are involved, issues regarding the most efficient choice of combinations of factor conditions and ability to detect interactions between factors become important.  With many factors and many possible experimental conditions for each factor, the scale of a comprehensive experimental design becomes impractical and suitable strategies for choosing informative subsets of the full design are needed.

The analysis of data resulting from well designed experiments is often very simple and graphical analysis can be very effective.  Standard statistical significance tests may be used to assure that apparent effects are real and not due simply to chance process variation.  Confidence intervals are used in estimating the magnitude of effects.  In cases with more complicated experimental structure, a more advanced technique of statistical inference, Analysis of Variance, may be used.

Minitab is well equipped to assist both with design set up and with analysis of subsequent data, both graphical and formal.  There will be two laboratory sessions involving the use of Minitab.

Case studies and illustrations from a range of substantive areas will be discussed

On successful completion of this module, students should be able to:

  • compare and contrast observational and experimental studies,
  • describe and explain the roles of control, blocking, randomisation and replication in experimentation,
  • explain the advantages of statistical designs for multifactor experiments,
  • describe and explain the genesis of basic experimental design structures,
  • implement  and interpret the analysis of variance for a selection of experimental designs,
  • describe the models underlying the analysis of variance for a selection of experimental designs,
  • produce and interpret graphs for data summary and model diagnostics,
  • provide outline descriptions of more elaborate designs and data analyses,
  • describe and discuss strategic issues involved in the design and implementation of experiments.

Specific topics addressed in this module include:

The need for experiments
experimental and observational studies
cause and effect
Basic design principles for experiments
Blocking (pairing)
Factorial structure
Standard designs
Randomised blocks
Two-level factors
Multi-level factors
Split units
Analysis of experimental data
Exploratory data analysis
Parameter estimation and significance testing
Analysis of variance
Statistical models, fixed and random effects
Model validation, diagnostics
Software laboratories
Review topics
Block structure and treatment structure
Repeated measures
Analysis of Covariance
Clinical trials
Response surface designs
Robust designs
Non-Normal errors
Strategies for Experimentation

Assessment One 3-hour examination


Mullins, E., Statistics for the Quality Control Chemistry Laboratory, Royal Society of Chemistry, 2003, particularly Chapters 4-5, 7-8.  Detailed coverage of much of the module, in a specific context.
Mead, R., The design of experiments: statistical principles for practical applications, Cambridge University Press, 1988. Comprehensive text, with extensive discussion of fundamentals.
Box, G.E.P., Hunter, J.S. and Hunter, W.G., Statistics for Experimenters, 2nd. ed., Wiley, 2005.  Includes many gems of wisdom from these masters of the genre, though not a course text.
Daniel, C., Applications of Statistics to Industrial Experimentation, Wiley, 1976.  Includes many gems of wisdom from this master of the genre, using methodology appropriate for an industrial setting.
Robinson, G.K., Practical Strategies for Experimenting, Wiley, 2000.  A comprehensive review of the non-statistical aspects of planning and conducting experiments and interpreting and using their results.

Module Description ST7003 Blackboard