School of Computer Science and Statistics. Postgraduate Certificate in Statistics

Transcription

1 School of Computer Science and Statistics Postgraduate Certificate in Statistics

2 SEPTEMBER, 2015 Dear Participant, Welcome to the Postgraduate Certificate in Statistics. This booklet contains some important information on various aspects of the course - dates of lecture terms, examination regulations and course outlines. So please take some time to read it. The course will be taught jointly by a group of people. See attached list of modules for information on the lecturers and content of the modules. I can be contacted at , Rozenn.Dahyot@scss.tcd.ie throughout the year if you have any queries or problems in relation to the academic side of the course. Natasha Blanchfield is the executive officer who administers the course; her number is , pgcert.stats@tcd.ie Administrative queries should be addressed to Natasha in the first instance. Note that we will want to communicate with you by from time to time. We will use the College address that you will be assigned (many of you will have these already in the form NAME@tcd.ie). s will NOT be sent to non-tcd addresses. It is important, therefore, that you check your college mail regularly (and frequently!). On a very small number of occasions (due to illness or extreme weather) lectures have been cancelled. I recommend, therefore, that before all lectures you check your to ensure a cancellation notice has not been sent out. It is also important that you register at the appropriate times if you do not, you may be charged a late registration fee. Unfortunately we are unable to provide parking on campus. I hope you enjoy the course. Best wishes, Rozenn Dahyot Course Director Disclaimers: The information contained in this document is intended to provide a guide to those seeking admission to the programme, and to the students on the course. Trinity College Dublin reserves the right to update or change syllabi, timetables, or other aspects of the programme at any time. Changes will be notified to current students by . 2

3 Table of Contents for Course Handbook. Postgraduate Certificate in Statistics ACADEMIC STRUCTURE... 4 EXAMINATIONS... 4 DISTINCTIONS... 4 PREVIOUS EXAMINATION PAPERS... 4 CALCULATORS IN EXAMINATIONS... 5 DEFERRING EXAMINATIONS... 5 INDIVIDUAL WORK AND PLAGIARISM... 5 TAUGHT MODULES... 6 MODULE DESCRIPTORS... 7 ST ST ST ST STAFF COURSE DIRECTOR COURSE ADMINISTRATION LECTURERS THE GRADUATE STUDENTS UNION THE POSTGRADUATE ADVISORY SERVICE TCD WEB LINKS / COLLEGE CONTACTS HEALTH & SAFETY NEW STUDENT INFORMATION SYSTEM (SITS) APPENDIX 1: PLAGIARISM CALENDAR ENTRY SCSS ASSESSMENT SUBMISSION SHEET APPENDIX 2: MAPS APPENDIX 3: TIMETABLE APPENDIX.4: ACADEMIC YEAR STRUCTURE TEACHING TERM DATES Michaelmas Term Hilary Term

4 Academic Structure The Postgraduate Certificate consists of a Base Module and a series of Elective Modules. The elective modules offered may vary from year to year. To obtain the Postgraduate Certificate participants must pass the Base Module and two Elective Modules. This is done within one academic year. To facilitate students registered on the PG Certificate in Statistics as their primary course who, due to work commitments, cannot do this, it is possible to take the course over two years. Note, though, that full fees are charged for the second year. Students who wish to take the Postgraduate Certificate course over two years must apply to do so before the end of the first semester. Examinations Each module will be examined separately. The form of the examination may vary from module to module and may include assignments, written examinations or both. Details will be given by the lecturers concerned. Students take the examinations for all modules during the annual examination period. This will be between Monday 2 nd May to Friday 27 th May Individual examination dates are set by the Examinations Office of the University and will not be available until the second semester. Examination timetables will be posted on the College website: No letters will be sent regarding this matter. Subject to the recommendation of the court of examiners, students who are unsuccessful in the annual examinations may be allowed a supplemental examination. The dates for the supplemental examination period will be confirmed when available, but usually take place at the end of August/beginning of September. Failure in the supplemental examination for the Base Module (i.e., on a second attempt) leads to exclusion from the course. Students who fail a supplemental examination in an elective module may take a different elective module (but only if one is available, which is not guaranteed) in the following year. This may be done only once, i.e., at most three elective modules may be attempted. Distinctions Each module will be graded as follows: fail (< 50%), pass (50%+). To obtain the Postgraduate Certificate with distinction, participants must obtain an overall average of at least 70%. The average is weighted using the ECTS units for the various modules. Distinctions are not awarded at supplemental examinations. Previous Examination Papers Copies of previous examinations papers are published at: Note that Supplemental Examination papers are not published. 4

5 Calculators in Examinations Note that any standard scientific calculator will be acceptable for use in the examinations. Devices, such as smartphones, that can store text are not allowed mobile phones may not be used as calculators. Our examinations papers are designed to examine understanding of ideas and do not tend to involve extensive calculations. Deferring Examinations In exceptional circumstances (e.g. illness, force majeure), permission may be granted by the Dean of Graduate Studies to defer examinations to the supplemental examination period. Contact the course administrator to organize the request to the Dean of Graduates Studies. Please note that inadequate preparation for the examinations is not a valid basis for a deferral. If a student cannot sit an examination for medical reasons, medical certificates must be submitted to the Course Administrator; the College regulations require that this be done within three days of the beginning of the period of absence from the examination. Where deferral is granted for either medical or non-medical reasons and the student fails the examination in the supplemental period, the student will need to ask permission to sit a supplemental examination for the failed module(s). This request is to be submitted by the course administrator to the Dean of Graduates Studies. Unexplained absence from examinations results in exclusion from the course. Individual Work and Plagiarism It is important to highlight that all work submitted must be your own, and not taken directly from the internet or other sources. The College takes plagiarism seriously. The College regulations governing plagiarism are available in the college calendar and are copied in Appendix 1. You are expected to be familiar with these rules and to understand what is considered plagiarism. Before beginning your first assignment, you must complete the online tutorial on avoiding plagiarism Ready, Steady, Write, located at You are also encouraged to use the College Library s repository of resources on plagiarism and its avoidance at In the case of group work, groups should establish some mechanism to ensure that no member engages in plagiarism. Do not sign the Group Assignment Declaration if you have not assured yourself that the whole assignment is original. 5

6 Taught Modules The modules being run this year are shown below. Module Code Module Name Module Coordinator Semester ECTS Assessment ST7001 Base Module Professor Gerard Keogh 1 15 Examination & optional Coursework ST7002 Introduction to Regression Professor Myra O Regan 2 10 Examination & optional Coursework ST7003 Design and Analysis of Experiments Professor Michael Stuart 2 10 Examination ST7005 Time Series Analysis Professor Rozenn Dahyot 2 10 Examination 6

7 Module Descriptors Module Code Module Name ST7001 Base Module (Post Graduate Certificate in Statistics) ECTS weighting 15 Semester/term taught Contact Hours Michaelmas Lecture hours:44 Lab hours:4 Total hours:48 Module Personne Lecturing staff: Gerard Keogh On successful completion of the Base Module students should be able to: demonstrate a systematic understanding of the fundamental inferential ideas which underpin statistical methods Learning Outcomes demonstrate a broad understanding of the role of statistical ideas and methods covering both data collection and data analysis demonstrate a competence in the use of basic statistical tools They will have a sound basis on which to develop further their statistical skills. Module Learning Aims The base module is introductory and will lay down the foundations on which other modules will build. The fundamental statistical inferential ideas of significance tests and confidence intervals are the central topics. The various inferential methods will be unified through the concept of a statistical model, which is an abstract representation of the quantity we wish to describe. For example, we may choose to represent the weights of filled containers by a Normal distribution with a particular centre (mean) and measure of spread (standard deviation). This would allow us to introduce formal tests to determine when the process average weight changes. Of course, the value of any formal procedure will depend on how well the underlying model represents the characteristics of the practical problem. When models are fitted, good statistical practice requires the assessment of the models used; this is done mainly by use of graphical procedures. These may be simple scatterplots of two characteristics of a number of individuals (e.g., heights and weights of a sample of people) to determine whether or not the assumption of a linear relationship between the two characteristics is 7

8 reasonable. Alternatively, the graph might be a Normal probability plot (quantile-quantile plot) of residuals (differences between observed and predicted values) after a complex multiple regression model has been fitted to the data. Many questions can be answered by simple plots, so the course will emphasis practical methods that can be applied across many empirical disciplines. Specific topics addressed in this module include: Data summaries and graphs Statistical models Sampling distributions: confidence intervals and tests Module Content 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 The course notes are extensive and are the primary source material needed for the course. The following book is a suitable general reference for the base module. D.S. Moore and G. P. McCabe, Introduction to the practice of statistics, Freeman, 5th edition, 2006 Recommended Reading List My own book was written for analytical chemists, but it would be suitable reading for most natural scientists and engineers. Moore and McCabe would be more suitable for social scientists. E. Mullins, Statistics for the quality control chemistry laboratory, Royal Society of Chemistry, Those with medical interests will find the following a useful reference book: D. G. Altman, Practical statistics for medical research, Chapman and Hall, Those interested in business and industry will find lots of interesting examples in:m. Stuart, "Introduction to Statistical Analysis for Business and Industry, a problem solving approach", Hodder Arnold Publishers, Assessment Details Exam: 100% 8

9 Module Code Module Name Module Short Title ST7002 Introduction to Multiple Linear Regression Regression ECTS weighting 10 Semester/term taught Hilary Lecture hours: 21 Contact Hours Lab hours: 3 Total hours: 24 Module Personnel Learning Outcomes Lecturing staff: Prof M. O Regan and demonstrators Students will have the ability to carry out an initial examination of the data to use a regression package (MINITAB) to apply multiple regression to simple data sets to interpret the results of the model to construct and exploit derived variables, such as logs, products and indicator variables to see such modelling as the basis for more advanced statistical analysis Module Learning Aims Multiple linear regression and its many variants is the most widely used tool in applied statistics. This course will build on simple linear regression, introduced in the Base Module. The aim is to become familiar with its use, to further develop experience and confidence in use and role of statistical modelling. As the class is diverse in terms of research area and quantitative skills, students are encouraged to conduct small analyses of data in their own research fields. 9

10 Module Content Specific topics addressed in this module include: Review of simple linear regression model: assumptions, model fitting, estimation of coefficients and their standard errors The multiple linear regression model and its analysis including: o Confidence intervals and statistical significance tests on model parameters o Issues in the interpretation of the multiple parameters o Analysis of variance in regression: F-tests, r-squared o Indicator variables and interaction terms Model validation: residuals, residual plots, normal plots, diagnostics Introduction to logistic regression Recommended Reading List Module Pre- Requisite Assessment Details The reading list will be available at a later date. Base Module ST7001. Exam, one optional project 25% The final grade will be max( exam/100, exam/75 + project/25) 10

11 Module Code Module Name ECTS weighting Semester/term taught ST7003 Design and Analysis of Experiments 10 2 Lecture hours:20 Contact Hours Lab hours:4 Tutorial hours:0 Total hours:24 Module Personnel Learning Outcomes Lecturing staff: Dr. Michael Stuart 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 a range of basic experimental design structures, implement and interpret the analysis of variance for a range of experimental designs, describe the models underlying the analysis of variance for a range of basic experimental designs, produce and interpret graphs for data summary and model diagnostics, provide outline descriptions of more elaborate designs and data analyses, outline strategic issues involved in the design and implementation of experiments. Module Learning Aims This module is concerned with the design of data collection exercises for the assessment of the effects of making deliberate changes to factors associated with a process or system 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 11

12 varied. Design principles intended to assure such control of experimental conditions are advocated. It should be noted that the nature and degree of control implicit in this description are frequently not attainable in the social sciences, where observational as opposed to experimental studies are the rule and alternative strategies are required to attempt to assure that observed changes caused observed effects. Such observational studies are not pursued in this module. 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. In cases with more complicated experimental structure, a more advanced technique of statistical inference, Analysis of Variance, may be used. Confidence intervals are used in estimating the magnitude of effects. Minitab may be used 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. Module Content The need for experiments experimental and observational studies cause and effect control Basic design principles for experiments Control Blocking (pairing) Randomisation Replication Factorial structure Standard designs Randomised blocks 12

13 Two-level factors Multi-level factors Split units Analysis of experimental data Exploratory data analysis Effect estimation and significance testing Analysis of variance Statistical models, fixed and random effects Model validation, diagnostics 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 Recommended Reading List Core material: Mullins, E., Statistics for the Quality Control Chemistry Laboratory, Royal Society of Chemistry, 2003, particularly Chapters 4-5, 7-8. Suggested Text: Montgomery, D.C., Design and analysis of experiments, 8th ed., Wiley, Alternative Text: Dean, Angela and Voss, Daniel, Design and analysis of experiments, Springer, Recommended Reading (not essential for examination purposes): Box, G.E.P, Hunter, J.S. and Hunter, W.G., Statistics for Experimenters, 2nd. ed., Wiley, Cox, D.R., Planning of Experiments, Wiley, Daniel, C., Applications of Statistics to Industrial Experimentation, Wiley, Fisher, R.A., The Design of Experiments, Oliver and Boyd, 1935, (8th ed. 13

14 1966). Mead, R., Gilmour, SG and Mead, A, Statistical Principles for the Design of Experiments: Applications to Real Experiments, Cambridge University Press, Robinson, G.K., Practical Strategies for Experimenting, Wiley, Supplemental lectures notes available on course website. Assessment Details % Exam:100 % Coursework:0 Description of assessment & assessment regulations. 14

15 Module Code Module Name ST7005 Time Series Analysis ECTS weighting 10 Semester/term taught Contact Hours Hilary term 2016 Lecture hours: 18 Lab/tutorial hours: 6 Total hours: 24 Module Personnel Learning Outcomes Lecturing staff: Rozenn Dahyot When students have successfully completed this module they should be able to: Define and describe the different patterns that can be found in times series and propose algorithms and statistical models that are suitable for their analysis. Program, analyse and select the best model for forecasting. Interpret output of data analysis performed by a computer statistics package. Compute predictions with their confidence intervals using the selected model. Module Learning Aims Module Content The aim of this module is to learn several mathematical techniques to analyse past observations for the purpose of predicting future outcomes and their associated uncertainty. The module will be practical, and will involve every student in extensive analysis of case study materials for a variety of time series data. Introduction to forecasting; ARIMA models, data transformations, seasonality, exponential smoothing and Holt Winters algorithms, performance measures. Use of transformations and differences. Recommended Reading List Forecasting - Methods and Applications, S. Makridakis, S. C. Wheelwright and R. J. Hyndman, Wiley 15

16 Module Pre Requisite Assessment Details Basic Statistics and Mathematics Exam: 100% 16

17 Staff Course Director Professor Rozenn Dahyot ) Tel: Course Administration Natasha Blanchfield ) Tel: Lecturers Professor Gerard Keogh ) Professor M O Regan (Myra.ORegan@scss.tcd.ie ) Tel: Professor M. Stuart (michael.stuart@tcd.ie ) Professor R Dahyot (Rozenn.Dahyot@tcd.ie ) Tel: The Graduate Students Union The GSU asked us to put the following statement in the handbook. Located on the second floor of House Six, the Graduate Students' Union is an independent body within College that represents postgraduate students throughout College. Upon registration, all postgraduates are automatically members. It is run by two full-time sabbatical officers; this year they are the President, Meg Lee, and the Vice- President, Adam Hanna. As the head and public face of the Union, Ryan is responsible for strategy and policy formulation, whilst sitting on a wide range of committees. Sarah is the Union's Education and Welfare Officer and advises students on matters such as academic appeals and supervisor relationships. She's also here to help on more personal matters, such as financial concerns, illness and bereavement. Any discussions about such concerns are treated with the strictest confidentiality. Contact us at either: president@tcdgsu.ie, or vicepresident@tcdgsu.ie." 17

18 The Postgraduate Advisory Service The Postgraduate Advisory Service is a unique and confidential service available to all registered postgraduate students at Trinity College. It offers a comprehensive range of academic, pastoral, and professional supports dedicated to enhancing your student experience. The Postgraduate Student Support Officer provides frontline support for all postgraduate students at Trinity College Dublin. S/he will act as a first point of contact and a source of support and guidance, both on your arrival in College and at any time during your stay. In addition to the dedicated Postgraduate Student Support Officer, each Faculty has three members of their academic staff, with substantive experience of research supervision, designated to provide local support to the postgraduate students for whom the Faculty is responsible. If you require specific advice, or would like to arrange a confidential meeting with the dedicated Student Support Officer, you can make an appointment by phoning , or by ing pgsupp@tcd.ie The service is located on the second floor of House 27 in the Senior Tutor s Office. TCD Web links / College Contacts There are many useful sites in TCD. Here are a number of them. If you find any other TCD links that you think would be useful for the class please the Course Administrator (pgcert.stats@tcd.ie ). Site Postgraduate Advisory Service pgsupp@tcd.ie Junior Dean and Registrar of Chambers Junior.Dean@tcd.ie TCD Website Library Information System Services Graduate Studies Student Counselling Computer Science and Statistics Address Health & Safety The Faculty of Engineering, Mathematics and Science Health and Safety Manual 2015/2016 is now available for download at the url below. There will be no printed booklets distributed this year and Declaration Forms A, B & C will now be forwarded to all incoming, registered students via and in Word format to ease completion. 18

19 New Student Information System (SITS) ACCESS VIA All communications from College will be sent to you via your online portal which will give you access to an intray of your messages. You will also be able to view your timetables online, both for your teaching and for your examinations. All fee invoices/payments, student levies and commencement fees will be issued online and all payments will be carried out online. You will be able to view your personal details in the new system some sections of which you will be able to edit yourself. Up until now, all examination results were published online by the Examinations Office at in future, it is planned that your results will also be communicated to you via the online portal. Future plans for the new system include online module registration and ongoing provision of module assessment results. 19

20 Appendix 1: Plagiarism Calendar Entry 20

21 SCSS Assessment Submission Sheet School of Computer Science and Statistics Assessment Submission Form Student Name Student ID Number Course Title Module Title Lecturer(s) Assessment Title Date Submitted Word Count I have read and I understand the plagiarism provisions in the General Regulations of the University Calendar for the current year, found at: I have also completed the Online Tutorial on avoiding plagiarism Ready, Steady, Write, located at I declare that the assignment being submitted represents my own work and has not been taken from the work of others save where appropriately referenced in the body of the assignment. Signed Date 21

22 Appendix 2: Maps Maps can be found online at Interactive map 22

23 Appendix 3: Timetable 23

24 Appendix.4: Academic Year Structure 24

25 Teaching Term Dates Michaelmas Term Mon 28 Sept 2015 Fri 18 Dec 2015 Week Sep - 02 Oct Week Oct - 09 Oct Week Oct - 16 Oct Week Oct - 23 Oct Week Oct* - 30 Oct Week Nov - 06 Nov Week Nov - 13 Nov - Study Week Week Nov - 20 Nov Week Nov - 27 Nov Week Nov - 04 Dec Week Dec - 11 Dec Week Dec - 18 Dec Hilary Term Mon 18 Jan Fri 08 April 2016 Week Jan - 22 Jan Week Jan - 29 Jan Week Feb - 05 Feb Week Feb - 12 Feb Week Feb - 19 Feb Week Feb - 26 Feb Week Feb - 04 Mar - Study Week Week Mar - 11 Mar Week Mar - 18 Mar Week Mar - 25 Mar* Week Mar* - 01 Apr Week Apr - 08 Apr *Public Holiday 25