Module Specifications.
Current Academic Year 2024 - 2025
All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).
As such, this is a point in time view of data which will be refreshed periodically. Some fields/data may not yet be available pending the completion of the full Coursebuilder upgrade and integration project. We will post status updates as they become available. Thank you for your patience and understanding.
Date posted: September 2024
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Repeat examination |
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Description MS228 aims to provide a strong foundation in the fundamental statistical method of regression modelling. Simple and multiple linear regression models will be presented. The fitting and interpretation of regression models will be explained and practical examples given. The linear model will be extended to model non normal data using generalised linear models (GLMs). The regression models will be applied to practical datasets using R. Students will also be introduced to Bayesian statistical methods and their use in credibility theory. | |||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Perform exploratory data analysis techniques on a sample of data. 2. Describe and interpret linear regression models and estimate the parameters of a simple linear regression model. 3. Describe and interpret generalised linear models (GLMs). 4. Fit linear and generalised linear models to datasets using R and apply appropriate model checks. 5. Understand the Bayesian approach to statistical modelling. 6. Apply the Bayesian and empirical Bayesian approaches to credibility theory. | |||||||||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and Learning Activities
Exploratory Data AnalysisData visualisation – histograms, box plots, scatter plots, heatmaps. Use of R visualisation packages. Correlation measures. Introduction to Principal Components Analysis. Use of R to implement analysis techniques.Linear RegressionExploratory data analysis; correlation; least squares estimation; model fitting in R; goodness of fit measures; residual checking; predication; confidence intervals.Multiple Linear RegressionModel definition; modelling fitting and checking in R.Generalised Linear Models (GLM)Exponential family - binomial, Poisson, exponential, gamma and normal; link function and canonical link function; deviance and scaled deviance; model fitting in R; Pearson Chi-square test and the Likelihood ratio test; model interpretation.Bayesian StatisticsBayes theorem; prior and posterior distributions; loss functions.Credibility TheoryCredibility premium; Bayesian approach to credibility theory; empirical Bayes approach to credibility theory. | |||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List | |||||||||||||||||||||||||||||||||||||||||||
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||