# DEGREE REGULATIONS & PROGRAMMES OF STUDY 2013/2014 Archive for reference only THIS PAGE IS OUT OF DATE

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DRPS : Course Catalogue : School of Philosophy, Psychology and Language Sciences : Psychology

# Postgraduate Course: Univariate Statistics and Methodology using R (PSYL11053)

 School School of Philosophy, Psychology and Language Sciences College College of Humanities and Social Science Course type Standard Availability Available to all students Credit level (Normal year taken) SCQF Level 11 (Postgraduate) Credits 10 Home subject area Psychology Other subject area None Course website Please use Learn Taught in Gaelic? No Course description This semester long course is taught using a combination of lab and lecture sessions and is suitable for students following Masters programmes in Psychology and Linguistics It takes students from introduction to basic statistics and an introduction to the basics of the R package, to competence in the standard univariate methodology and analysis using R. R is a language and environment for statistical computing and graphics, based on the S language. R is a flexible and increasingly popular package for statistical analysis. It provides a wide variety of statistical and graphical techniques, including facilities to produce well-designed publication-quality plots. Design and analysis are taught under a unifying framework which shows a) how research problems and design should inform which statistical method to use and b) that all statistical methods are special cases of a more general model. The course concentrates on research designs and analysis for problems in which there is a single outcome variable and would be taught using the general linear model as a framework to design and analysis. Typical Syllabus: * Methods of science and refresher on descriptive * Introduction to R statistics * Refresher in inferential statistics including Hypothesis testing, Type I vs. Type II errors, p-values, power, correlation, chi-squares, linear regression * Introduction to data analysis using R * Multiple regression * Multiple regression analysis using R * The general linear model (GLM) including continuous DV models, including ANOVA, ANCOVA, mixed designs, Defries-Fulker analysis * General linear models with continuous DVs in R Formative feedback; - the lab practicals are formative.
 Pre-requisites Co-requisites Prohibited Combinations Other requirements None Additional Costs None
 Pre-requisites None Displayed in Visiting Students Prospectus? No
 Delivery period: 2013/14 Semester 1, Available to all students (SV1) Learn enabled:  Yes Quota:  100 Web Timetable Web Timetable Course Start Date 16/09/2013 Breakdown of Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 22, Supervised Practical/Workshop/Studio Hours 5, Feedback/Feedforward Hours 1, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 70 ) Additional Notes Breakdown of Assessment Methods (Further Info) Written Exam 0 %, Coursework 0 %, Practical Exam 100 % No Exam Information
 - Students should know which statistical analyses are appropriate to the research design of particular studies. - Understand how a common framework unifies seemingly disparate data analysis methods. - Use the R statistical package to analyze real data, be able to interpret the outputs, and create any required graphs.
 End of course assignment: a data analysis exercise (take home exam) 70% Assignment deadline: Monday 20th January 2014 by 12 noon Page limit: 6 pages for the write-up (4 pages of text and 2 pages of tables/figures). There is no limit for the code. We consider the completion of lab work sheets and take home mini assignments to be an essential preparation for the exam. All components must be submitted in order to pass the course. 30% Homework Deadlines: Monday 7th October, 10.00 am Monday 14th October, 12 noon Monday 21st October, 12 noon Monday 28th October, 12 noon Monday 4th November, 12 noon Monday 11th November, 12 noon Monday 18th November, 12 noon Monday 25th November, 12 noon
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 Academic description Not entered Syllabus Not entered Transferable skills Not entered Reading list Not entered Study Abroad Not entered Study Pattern Not entered Keywords Not entered
 Course organiser Dr Adam Moore Tel: (0131 6)50 3369 Email: amoore23@exseed.ed.ac.uk Course secretary Miss Toni Noble Tel: (0131 6)51 3188 Email: Toni.noble@ed.ac.uk
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