Postgraduate Course: Statistical Modelling (MATH11039)

Course Outline
School	School of Mathematics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 11 (Postgraduate)	Availability	Not available to visiting students
SCQF Credits	5	ECTS Credits	2.5
Summary	Goodness-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 Modelling		2:00

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.