Linear Models
MODULE CODE
CREDIT VALUE
Module Aims
Aim 1
Introduce students to the theory of generalised linear models for modelling relationships between variables, with an emphasis on practical considerations.
Aim 2
Present to students the use of specific models and associated techniques.
Aim 3
Train students to use statistical software packages such as SPSS (or STATA).
Module Content
Linear Models I
Simple Linear Regression Model: Estimation of the parameters, Confidence Intervals and Hypothesis testing for the parameters.
Multiple Linear Regression Model: Estimation of the parameters, Confidence Intervals and Hypothesis testing for the parameters
Model Adequacy: Residual analysis, Tests for linearity, homoscedasticity, independence, normality, lack-of-fit test, Model Selection Criteria.
Linear Models II
Analysis of Variance: Single and Multi-factor ANOVA with fixed effects, Single and Multi-factor ANOVA with random effects, Multiple Comparisons: Bonferroni’s Method, Tukey’s Method.
Analysis of Covariance
Generalised Linear Models: Logistic Regression, Probit Models, Poisson Regression.
Statistical Software Package (SPSS/STATA)
Describing Data, Computing Descriptive Statistics, Comparing Groups, Plotting Data, Testing Hypothesis, One-way ANOVA, Two-way ANOVA, Linear Regression and Correlation, Testing Regression Hypothesis, Analysing Residuals, Multiple Regression Models.
Learning Outcomes
On successful completion of this module, a student will be able to:
Teaching Methods
The class contact will consist of teaching classes together with workshops. Teaching classes will introduce new material and provide examples. Tutorials have no new material introduced. Students will attempt problems during the tutorials. Key elements of the learning strategy are regular sessions during which problems are attempted. Throughout the week students will be given a list of problems to attempt.
The module will be assessed principally by examination. However, to facilitate and monitor the formative learning process selected set exercises will be submitted for assessment. These will present regular opportunities for feedback and feedforward. At the end of the module, students will be expected to include a reflective component in this portfolio of work. This will make up the coursework component of the module.
Assessment Methods
The module is assessed through a Portfolio of set exercises and a written examination.
View The Other Course’s Modules
Compulsory
Optional
