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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2015/2016

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DRPS : Course Catalogue : School of Social and Political Science : School (School of Social and Political Studies)

Undergraduate Course: Statistical Modelling (SSPS10027)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course covers generalized linear models, some major statistical learning tools, and models for complex causal relationships, mainly in the context of social sciences. Lectures are combined with practical computer lab tutorials in order to illustrate the applications of the theoretical tools. The analysis is carried out using the statistical software environment R, which is freely available under the GNU General Public License.
Course description The course employs a hands-on approach through analysis using the statistical software R. The applications are mostly chosen from real social science research questions but examples from other disciplines like biology, medicine and engineering are also given.

There are three main objectives of this course:
Provide a unifying framework for linear models through generalized linear models framework.
Introduce some common learning algorithms. (Dimensionality reduction techniques such as PCA and factor analysis, clustering algorithms, and discriminant analysis will be discussed.)
Introduce specific models to deal with complex causal relationships (IV regression and selection model)

On top of the theoretical tools introduced, the course aims to equip students two other computational skills: data management and data visualization. R packages dplyr and ggplot2 will be introduced and used for these purposes.

Topics typically covered include:
Data Management and Visualization
Generalized Linear Models
Unsupervised Learning (PCA/Explanatory Factor Analysis, Clustering)
Supervised Learning (Discriminant Analysis)
Selection Models (Heckman)
Instrumental Variable Regression
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Doing Social Research with Statistics (SSPS08007)
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2015/16, Available to all students (SV1) Quota:  20
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 22, Seminar/Tutorial Hours 11, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 163 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Eight weekly exercises (40% in total)
End-of-course practical project (60%)
Feedback Eight weekly exercises. This would typically involve a quiz in class using clickers.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Have a unified conceptual and mathematical understanding of linear models
  2. Be able to use the R software for data management, data analysis and data visualization.
  3. Be able to analyze multidimensional data through dimension reduction, clustering and discriminant analysis.
  4. To appreciate the uses and limits maximum likelihood estimation.
  5. Be able to deal with particular endogeneity and omitted variable bias problems.
Reading List
None
Additional Information
Graduate Attributes and Skills Developing advanced quantitative skills and the capacity to use them in applied scientific context.
KeywordsStatistical analysis; regression; generalized liner models; statistical learning; R
Contacts
Course organiserDr Ugur Ozdemir
Tel: (0131 6)50 3990
Email: Ugur.Ozdemir@ed.ac.uk
Course secretaryMr Daniel Jackson
Tel: (0131 6)50 3932
Email: Daniel.Jackson@ed.ac.uk
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