STU34501 – Applied Linear Statistical Methods I

Source: https://teaching.scss.tcd.ie/module/stu34501-applied-linear-statistical-methods-i/ Parent: https://www.maths.tcd.ie/undergraduate/modules/minor-stats.php

Offered in 2025/26


Module Code	STU34501
Module Name	Applied Linear Statistical Methods I
ECTS Weighting [1]	5 ECTS
Semester taught	Semester 1
Module Coordinator/s	Dr. Jason Wyse

Module Learning Outcomes


On successful completion of this module, students will be able to: LO1. Derive least squares estimators for a linear regression model LO2. Derive and use properties of least squares estimators for inference LO3. Extend the linear model to the general linear model (one way classification, polynomial regression) including use of dummy variables LO4. Carry out model diagnostics through analysis of residuals LO5. Form a Bayesian linear model and appreciate connections with ridge regression LO6. Demonstrate how regularization can be used for model determination through the LASSO

On successful completion of this module, students will be able to: LO1. Derive least squares estimators for a linear regression model LO2. Derive and use properties of least squares estimators for inference LO3. Extend the linear model to the general linear model (one way classification, polynomial regression) including use of dummy variables LO4. Carry out model diagnostics through analysis of residuals LO5. Form a Bayesian linear model and appreciate connections with ridge regression LO6. Demonstrate how regularization can be used for model determination through the LASSO

Module Content

Working with linear and generalized linear models is an essential part of a data analyst’s work. This module presents the theory of the normal linear model and links this with the use of this theory in practice through examples in R. Diagnosing the fit (and hence appropriateness) of a model through residual analysis is discussed. The final part of the module looks at the more modern topic of regularization. This is motivated first through looking at the Bayesian linear model and its connections with ridge regression, then model determination through the Least Absolute Shrinkage and Selection Operator (LASSO) is discussed.

Teaching and learning Methods

There will be three classes per week. Some of these classes will be used for code demonstrations and tutorials.

Assessment Details


Assessment Component	Brief Description	Learning Outcomes Addressed	% of total	Week set	Week Due
Examination	End of semester exam (2 hours)	LO1-LO6	90%	N/A	N/A
Assignments	Four assignments throughout the semester	LO1-LO6	10%	3,5,7,9	4,6,8,10

Reassessment Details

100% supplemental exam (2 hours)

Contact Hours and Indicative Student Workload


Contact Hours (scheduled hours per student over full module), broken down by:	33 hours
Lecture	33
Independent study (outside scheduled contact hours), broken down by:	82 hours
Preparation for classes and review of material (including preparation for examination, if applicable	42
completion of assessments (including examination, if applicable)	40
Total Hours	115 hours


Linear Regression Analysis, Seber, G. A. F. and Lee, A. J. (2003), Wiley Series in Probability and Statistics, 2nd edn, Wiley, Hoboken, NJ Pattern Recognition and Machine Learning, Christopher Bishop, Springer Applied Linear Statistical Models, Michael Kutner, Christopher Nachtsheim, John Neter and William Li, McGraw-Hill/Irwin Computer Age Statistical Inference, Algorithms, Evidence and Data Science, Bradley Efron and Trevor Hastie, Cambridge University Press

Module Pre-requisites

STU23501

Module Co-requisites

None

Module Website

Blackboard