Undergraduate Course: Statistical Modelling (SSPS10027)
|School||School of Social and Political Science
||College||College of Humanities and Social Science
|Credit level (Normal year taken)||SCQF Level 10 (Year 3 Undergraduate)
||Availability||Available to all students
|Summary||This 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.
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.
The course will provide a unifying framework for linear models through generalized linear models framework ad Ántroduce some common learning algorithms. (Dimensionality reduction techniques such as PCA and factor analysis, clustering algorithms, and discriminant analysis will be discussed.)
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 with R
Generalized Linear Models
Unsupervised Learning (PCA/Explanatory Factor Analysis, Clustering)
Supervised Learning (Discriminant Analysis)
Entry Requirements (not applicable to Visiting Students)
|| Students MUST have passed:
Doing Social Research with Statistics (SSPS08007)
||Other requirements|| For those students who are required to take a Quantitative Methods course as part of their degree programme, this course can be counted towards that condition.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2018/19, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Seminar/Tutorial Hours 9,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Eight weekly exercises (40% in total)
End-of-course practical project (60%)
||The feedback and guidance will be provided during the course convenor's weekly office hours and by appointment. Students will furthermore be given written feedback on their tutorial assessments which will take place on a weekly basis throughout the course and before they are required to take the timed assignment. For the timed assignment, feedback will be provided in written form.
|No Exam Information
On completion of this course, the student will be able to:
- Have a unified conceptual and mathematical understanding of linear models
- Be able to use the R software for data management, data analysis and data visualization.
- Be able to analyze multidimensional data through dimension reduction, clustering and discriminant analysis.
- To appreciate the uses and limits maximum likelihood estimation.
- Be able to deal with particular endogeneity and omitted variable bias problems.
|Graduate Attributes and Skills
||Developing advanced quantitative skills and the capacity to use them in applied scientific context.
|Keywords||Statistical analysis; regression; generalized liner models; statistical learning; R
|Course organiser||Dr Ugur Ozdemir
Tel: (0131 6)50 3990
|Course secretary||Mr Euan Morse
Tel: 0131 (6)51 1137