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Statistics (P.Grad.Dip) – (Grad.Dip.)

University of Dublin Trinity College

School of Computer Science and Statistics
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Disciplines:
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Application Deadline: 13 August
Annual Tuition Fee: ≈ € 4,000 ≈ € 8,000 (non-EEA)
Location: Dublin / Ireland / View location on map ▾ Hide location on map ▴
Duration: 12 months Start Date: September
Educational Form:
  • Taught
Education Variants:
  • Parttime
Languages: English 
-6.25508,53.3439

Location of University of Dublin Trinity College

The School of Computer Science and Statistics at Trinity College, Dublin offers, in each academic year, a part time course leading to the Postgraduate Diploma in Statistics. The course provides a broad introduction to the statistical ideas and methods relevant to data gathering and analysis in a wide variety of research areas as well as business and administration. The intention is to provide participants with a practical grasp of statistics based on a sound knowledge of the underlying ideas and concepts. Graduates of the course should be well placed to apply the ideas and methods to which they have been introduced in their own work. To this end, all the material is presented in the context of practical examples from a wide range of applications.

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.

Academic Level
The Diploma 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.


Contents

All students take the Base Module, which is taught on Mondays and Wednesdays during the first 12 week semester, before Christmas. Students must then take two elective 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) and a module on Survey design (Mondays and Wednesdays). These will be followed by a six-week module on the Design and Analysis of Experiments.

ST7001: Base Module
Topics Covered: o Data summaries and graphs
o Statistical models
o Sampling distributions: confidence intervals and tests
o Simple comparative experiments: t-tests, confidence intervals, design issues
o Counted data: confidence intervals and tests for proportions
o Cross-classified frequency data: chi-square tests
o Introduction to Regression Analysis
o Introduction to Analysis of Variance
o Statistical computing laboratory

ST7002: Introduction to Regression
Topics covered:

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

ST7003: Design and Analysis of Experiments
Topics covered: o Experimental and observational studies
o Data collection and study design
o Basic designs for experiments
o Randomisation, pairing and blocking
o Exploratory data analysis for experimental data
o Introduction to analysis of variance
o Parameter estimation and significance testing
o Model validation
o Statistical computing laboratories

ST7004: Aspects of Survey Design

This module will focus on data collection using questionnaires. The following topics will be addressed on the course.

o The survey process
o Sample selection
o Measurement issues
o Designing paper and web based questionnaires
This module will include a computer laboratory element that will show students how to develop online questionnaires.

The model will be assessed on the basis of a project. Each student will be required to carry out a survey of their choice and write a detailed report on the development of the questionnaire. The number of students is limited to 30. Should more than 30 students apply, participants will be chosen at random.

IELTS

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Most European Universities recognise the IELTS test.

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Requirements

Applications are made online for this programme. During the application process you may be notified to send references to PAC.

English language requirements:

* IELTS: Grade 6.5
* TOEFL: 88 iBT, 230-computer based, 570 paper based
* Cambridge Certificate of Advanced English: Grade C
* Cambridge Certificate of Proficiency in English: Grade C

Language Proficiency

IELTS Band: 6.5
Cambridge English: Advanced (CAE): Grade A (Score: 80)
TOEFL Paper-based: 570
TOEFL Computer-based: 230
TOEFL Internet-based: 88

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