Undergraduate Course: Mathematics for Social Science (SSPS08009)
Course Outline
| School | School of Social and Political Science |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Laying Mathematical Foundations for Quantitative Methods
This course aims to provide students in the with Quantitative Methods programmes with the mathematical foundations, which will allow them to fully explore advanced methods, as well as gain a full understanding of the mathematic principles behind the basic methods. Throughout the course, the application of mathematics to social science research problems will be emphasised.
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| Course description |
Course Programme: Mathematics for Social Science
In the first part (weeks 1-5), the course will address linear relationships, integration and differential equations and least squares estimation of slope and intercept.
In the second part (weeks 6-8), the course looks beyond linearity to quadratic, exponential and logarithmic functions. This will include eigenvalues and eigenvectors, and principal components. The algebra of basic probability will be included here, laying the basis for dealing mathematically in later years of the programme with statistical distribution functions and with statistical inference.
In the third and final part (weeks 9-10), students will cover issues to do with curve sketching and analysis of residuals. They will also gain an understanding of the applications and relevance of the mathematical principles covered for future quantitative analysis methods and statistical inference.
Course Programme ¿ Overview
Part 1: Straight lines
Week 1
Introduction to Probability and Probability Distributions (AK)
Week 2
Gradients, equations and graphs of straight lines (VS)
Week 3
Tangent lines, differentiation (VS)
Week 4
Derivative functions, integration, (VS)
Week 5
Least squares estimation of slope and intercept (MT)
Part 2: Beyond linearity
Week 6
Graphs of quadratic functions, the solution of quadratic equations (AK)
Week 7
Exponential and logarithmic functions and common social science applications(VS)
Week 8
Eigenvalues and eigenvectors, and principal components (LP)
Part 3: Looking Ahead
Week 9
Curve sketching (MT)
Week 10
Summary and relevance for social sciences (VS)
Week 11
****NO SEMINAR ¿ Revision***
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | While entry to this course normally requires a pass at B in Mathematics at SQA Higher or A-level, students with confidence in their level (high school equivalent) of mathematical knowledge will be considered for admission. Please contact the course convenor if would like to join the course but have any concerns about your current Mathematical knowledge being sufficient |
Course Delivery Information
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| Academic year 2015/16, Not available to visiting students (SS1)
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Quota: None |
| 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 )
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| Assessment (Further Info) |
Written Exam
85 %,
Coursework
15 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
15% continuous assessment based on the best four out of five fortnightly tutorial assignments. This will constitute a formative feedback event. 85% written open-book two hour exam at the end of the course. |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S1 (December) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Provide students with mathematical foundations to understand advanced statistical methods
- Cover key mathematical principles in an applied context, using social science examples and real data
- Understand the mathematics behind least squares estimation; principal components; and logistic regression
- Understand how establishing statistical certainty relies on differential and integral calculus
- To engage critically with the challenges in capturing and understanding the world with quantitative methods
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Reading List
Students will be invited to make use of both on-line resources and books.
http://www.socialsciences.manchester.ac.uk/subjects/economics/postgraduate-taught/pre-session-maths/
Croft, A. and Davison, R. 2006. Foundation Maths. 4th ed., Longman.
Haeussler, E.F., Paul, R.S. and Wood,R., 2014. Mathematical Analysis for Business, Economics and the Life and Social Sciences, 13th ed., Pearson
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Prof Lindsay Paterson
Tel: (0131 6)51 6380
Email: Lindsay.Paterson@ed.ac.uk |
Course secretary | Mr Daniel Jackson
Tel: (0131 6)50 3932
Email: Daniel.Jackson@ed.ac.uk |
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© Copyright 2015 The University of Edinburgh - 18 January 2016 4:53 am
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