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STAT20230

Academic Year 2026/2027

Modern Regression Analysis (STAT20230)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
2 (Intermediate)
Credits:
5
Module Coordinator:
Dr Luiza Piancastelli
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

The course is intended to be a (non-exhaustive) survey of regression techniques from both a theoretical and applied perspective. Time permitting, the methods we will study include:
1. Exploratory data analysis
2. Simple Linear Regression (properties of least squares; t-test; F-test; R-squared; Confidence and Prediction Intervals)
3. Multiple Linear Regression (properties of least squares; t-test; F-test; R-squared; Confidence and Prediction Intervals)
4. Regression with Categorical Variables
5. Regression with Interaction terms
6. Polynomial Regression
7. Model Selection for Multiple Linear Models
8. Regression Diagnostics

About this Module

Learning Outcomes:

By the end of the course, students should be able to:
Enter tabular data using R.
Plot data using R, to help in exploratory data analysis.
Formulate regression models for the data, while understanding some of the limitations and assumptions implicit in using these models.
Fit models using R and interpret the output.
Test for associations in a given model.
Use diagnostic plots and tests to assess the adequacy of a particular model.
Find confidence intervals for the effects of different explanatory variables in the model.
Use some basic model selection procedures, as found in R, to find a best model in a class of models.
Fit simple ANOVA models in R, treating them as special cases of multiple regression models.

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


 

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

65
Lectures

24
Tutorial

10
Computer Aided Lab

11
Total

110

Approaches to Teaching and Learning:
Weekly Lectures;
Weekly Labs from week 2 onwards covering the implementation of the material in R;
Weekly tutorials from week 2 onwards;

One Assignment
One Exam

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade Component repeat (in-module) Offered
Exam (In-person): Midterm examination, one hour Week 7 Graded No
20
 No
Quizzes/Short Exercises: Quiz every two weeks in Brightspace, to be done after lab session
5 in total, 2% each		 Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Graded No
10
 No
Exam (In-person): End of term exam End of trimester
Duration:
2 hr(s)
 Standard conversion grade scale 40% No
70
 No

As part of UCD's student support, under the Additional Consideration policy, extensions may be available for the following assessments in the module: Assignment (including essay/poster), Portfolio, Reflective Assignment, Report(s), and Individual Project.


Carry forward of passed components
No
 

Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

All books are available in the library:
1. Applied Regression Analysis and Generalized Linear Models by John Fox
2. Linear Models with R by J.Faraway
3. An R Companion to Linear Statistical Models by Christopher Hay-Jahans

Name Role
Paul Lacotte Tutor
Dr Fabian Ofurum Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Fri 10:00 - 10:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Tues 09:00 - 09:50
Autumn Tutorial Offering 1 Week(s) - 3, 5, 7, 9, 10, 11 Tues 11:00 - 11:50
Autumn Tutorial Offering 2 Week(s) - 3, 5, 7, 9, 10, 11 Thurs 15:00 - 15:50
Autumn Tutorial Offering 3 Week(s) - 3, 5, 7, 9, 11 Fri 11:00 - 11:50
Autumn Computer Aided Lab Offering 1 Week(s) - 2, 4, 6, 8, 10 Mon 14:00 - 14:50
Autumn Computer Aided Lab Offering 2 Week(s) - 2, 4, 6, 8, 10 Tues 10:00 - 10:50
Autumn Computer Aided Lab Offering 3 Week(s) - 2, 4, 6, 8, 10 Thurs 12:00 - 12:50
Autumn Computer Aided Lab Offering 4 Week(s) - 2, 4, 6, 8, 10 Tues 10:00 - 10:50
Autumn Computer Aided Lab Offering 5 Week(s) - 2, 4, 6, 8, 10 Mon 10:00 - 10:50
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