Module Details

Module Code: STAT9004
Title: Statistical Data Analysis
Long Title: Statistical Data Analysis
NFQ Level: Expert
Valid From: Semester 2 - 2024/25 ( January 2025 )
Duration: 1 Semester
Credits: 5
Field of Study: 4620 - Statistics
Module Delivered in: 1 programme(s)
Module Description: In this module, the learner will study statistical techniques, with particular emphasis on linear models. 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 establish a data analysis protocol for data science problems.
LO2 Explain and apply the statistical concepts relevant to experimental design and data analysis with an emphasis on large data sets.
LO3 Build and validate statistical models with continuous response variables and multiple predictors (both categorical and continuous) using ANOVA, multiple regression and ANCOVA.
LO4 Distinguish between parametric and non-parametric methods and decide when the most commonly used non-parametric methods should be applied.
LO5 Build and validate statistical models with categorical response variables using logistic regression.
LO6 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
Exploratory data analysis: graphical and numerical methods to explore categorical and continuous data sets, outlier detection, 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. Statistical power and multiple comparisons. Non-parametric alternatives.
Multiple Regression
Assumptions, collinearity, interpreting coefficients, model fitting, model diagnostics, confidence intervals of coefficients, Analysis of covariance (ANCOVA).
Generalised Linear Models
Definition of a generalized linear model: link functions. 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 10
Timing Week 5 Learning Outcomes 1,2,3,4
Assessment Description
In class assessment.
Assessment Type Critique % of Total Mark 25
Timing Week 7 Learning Outcomes 1,2,3,4,5,6
Assessment Description
Group work assignment to explore and critique methods for developing statistical models. The group will create guidelines for critiquing statistical models and apply them to evaluate a published study where a statistical model is developed and validated. The findings will be delivered through a group presentation.
Assessment Type Practical/Skills Evaluation % of Total Mark 25
Timing Week 10 Learning Outcomes 1,2,3,6
Assessment Description
Solve and analyse problems in the laboratory setting.
Assessment Type Short Answer Questions % of Total Mark 40
Timing Week 13 Learning Outcomes 1,2,3,4,5,6
Assessment Description
In class written assessment
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

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 Formal lectures describing the theory underpinning the statistical techniques covered by the learning outcomes. Every Week 2.00 2
Lab Contact A series of laboratory exercises where the student will use a statistical software package to analyse data sets using the statistical techniques covered by the learning outcomes. Every Week 2.00 2
Independent Learning Non Contact Independent learning 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 Formal lectures describing the theory underpinning the statistical techniques covered by the learning outcomes. Every Week 1.50 1.5
Lab Contact A series of laboratory exercises where the student will use a statistical software package to analyse data sets using the statistical techniques covered by the learning outcomes Every Week 1.50 1.5
Lecturer Supervised Learning (Non-contact) Non Contact Lecturer Supervised Learning 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
  • Frank Harrell. (2015), Regression Modeling Strategies, 2. Springer International Publishing, [ISBN: 9783319194257].
  • Peter Dalgaard. (2008), Introductory Statistics with R, Springer, New York, [ISBN: 9780387790534].
Supplementary Book Resources
  • Michael J. Crawley. (2012), The R Book, Wiley-Blackwell, [ISBN: 978-0470973929].
  • Annette J. Dobson. (2018), An Introduction to Generalized Linear Models, Second Edition, 4. Chapman and Hall, [ISBN: 9781138741515].
Recommended Article/Paper Resources
Supplementary Article/Paper Resources
  • Collins G. S. et al.. (2024), TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods, British Medical Journal, 385,
    https://doi.org/10.1136/bmj-2023-078378
  • Wynants L. et al.. (2020), Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal, British Medical Journal, 369,
    https://doi.org/10.1136/bmj.m1328
This module does not have any other resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SDAAN_9 Master of Science in Data Science & Analytics 2 Mandatory