Module Details

Module Code: STAT8011
Title: Regression Analysis
Long Title: Regression Analysis
NFQ Level: Advanced
Valid From: Semester 1 - 2021/22 ( September 2021 )
Duration: 1 Semester
Credits: 5
Field of Study: 4620 - Statistics
Module Delivered in: 3 programme(s)
Module Description: In this module the learner will study statistical techniques, with particular emphasis on large data sets. Statistical analytical software such as R will be used in the labs.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Explore data sets and select appropriate statistical methods for data science problems.
LO2 Apply the concepts of Design of Experiments and analyse associated sets of data.
LO3 Analyse data sets with continuous response variables and multiple predictors (both categorical and continuous) using ANOVA, multiple regression and ANCOVA.
LO4 Analyse data sets with binary response variables using logistic regression.
LO5 Interpret the results of statistical analyses performed by a software package or presented in research papers.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Data Analysis Protocol
Consolidate prior knowledge of graphical and numerical descriptive statistics to explore categorical and continuous data sets. Outliers, missing values, testing of assumptions and transformation of variables. Model fitting and model interpretation. Model diagnostics.
Design of Experiments
Observational (vs) experimental data. The fundamentals of experimental design. Analysis of variance. Factorial design.
Multiple Regression
Assumptions, collinearity, interpreting coefficients, model fitting, model diagnostics, confidence intervals of coefficients, Analysis of covariance (ANCOVA).
Logistic Regression
Overview of different types of generalised linear models and their uses with a focus on logistic regression for binary data.
Software analysis
SPSS, R, Excel
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Short Answer Questions % of Total Mark 25
Timing Week 7 Learning Outcomes 1,2,3,5
Assessment Description
Theory Assessment - Design of Experiments and ANOVA
Assessment Type Short Answer Questions % of Total Mark 25
Timing Week 11 Learning Outcomes 1,3,4,5
Assessment Description
Theory Assessment - multiple regression and logistic regression
Assessment Type Project % of Total Mark 50
Timing Sem End Learning Outcomes 1,3,5
Assessment Description
Analyse (large) data set(s) and report results.
No End of Module Formal Examination
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Module Content delivery Every Week 2.00 2
Lab Contact Labs Every Week 2.00 2
Independent Learning Non Contact Study, practice and completion of worksheets Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Module Content delivery Every Week 1.50 1.5
Lab Contact Lab Every Week 1.50 1.5
Independent Learning Non Contact Study, practice and completion of worksheets Every Week 4.00 4
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Michael J. Crawley. (2012), The R Book, Wiley-Blackwell, [ISBN: 978-0470973929].
  • Julian J. Faraway. (2014), Linear Models with R, Chapman and Hall, [ISBN: 9781439887332].
Supplementary Book Resources
  • Frank Harrell. (2015), Regression Modeling Strategies, 2. Springer International Publishing, [ISBN: 9783319194257].
  • Annette J. Dobson. (2018), An Introduction to Generalized Linear Models, Second Edition, 4. Chapman and Hall, [ISBN: 9781138741515].
  • Trevor Hastie, Robert Tibshirani, Jerome H. Friedman. (2009), The Elements of Statistical Learning, Second. Springer, [ISBN: 978-038784857].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SPRDA_8 Certificate in Process Data Analytics 1 Mandatory
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 2 Mandatory
CR_EMENG_9 Master of Engineering in Mechanical Engineering 1 Group Elective 1