# 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**

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| --- | --- |
| **Module Code** | STU34501 |
| **Module Name** | Applied Linear Statistical Methods I |
| **ECTS Weighting [**[1]**](#_ftn1)** | 5 ECTS |
| **Semester taught** | Semester 1 |
| **Module Coordinator/s** | Dr. Jason Wyse |

## Module Learning Outcomes

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| --- |
| 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

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| --- | --- | --- | --- | --- | --- |
| **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** |

## Recommended Reading List

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| --- |
| 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