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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2015/2016

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: Statistical Modelling (MATH11039)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits5 ECTS Credits2.5
SummaryGoodness-of-fit tests: parametric using chi-squared test, non-parametric using Kolmogorov-Smirnov and graphical using probability plots. Multiple regression: continuous response and continuous explanatory variables, model diagnostics, continuous response and discrete-explanatory variables, continuous response and mixed continuous and discrete explanatory variables. Model building: variable selection, stepwise regression and multicollinearity. Logistic regression with binary response variable and continuous explanatory variables. The statistical software package SPSS will be used for practical instruction.
Course description Week 1 - Goodness-of-fit tests
Week 2 - Multiple Regression
Week 3 - Multiple Regression / Model Building
Week 4 - Model Building
Week 5 - Logistic regression
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2015/16, Available to all students (SV1) Quota:  None
Course Start Block 4 (Sem 2)
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 50 ( Lecture Hours 10, Supervised Practical/Workshop/Studio Hours 6, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 1, Directed Learning and Independent Learning Hours 31 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) See 'Breakdown of Assessment Methods' and 'Additional Notes', above.
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)MSc Statistical Modelling2:00
Learning Outcomes
Ability to use SPSS to fit models and interpret output. Versatility in the development and assessment of model structures. Ability to calculate statistics and model outcomes. Response variables may be continuous or binary and explanatory variables may be discrete or continuous.
Reading List
None
Additional Information
Course URL http://student.maths.ed.ac.uk
Graduate Attributes and Skills Not entered
KeywordsSTAM
Contacts
Course organiserDr Ioannis Papastathopoulos
Tel: (0131 6)50 5020
Email:
Course secretaryMrs Frances Reid
Tel: (0131 6)50 4883
Email:
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