Module Code - Title:

MS4215 - ADVANCED DATA ANALYSIS

Year Last Offered:

2025/6

Hours Per Week:

Lecture

2

Lab

1

Tutorial

0

Other

0

Private

6

Credits

6

Grading Type:

PF

Prerequisite Modules:

MS4213

MS4214

Rationale and Purpose of the Module:

Applies the theory developed in MS4213 and MS4214 to the development of advanced data analytic methods with particular emphasis on linear models. Students are introduced to a range of statistical packages.

Syllabus:

Simple Linear Regression : calibration, reverse prediction, regression through the origin, analysis of residuals, regression diagnostics, leverage and influence. Matrix formulation of the linear model : Multiple regression, partial correlation, polynomial regression. Analysis of Variance : One-way ANOVA, multiple comparisons, Two-way ANOVA, interactions, Analysis of covariance. Introduction to Generalized Linear Models including nonlinear regression, logistic regression and log-linear models.

Learning Outcomes:

Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)

Use Simple Linear Regression for calibration and reverse prediction, Apply and evaluate regression diagnostics with emphasis on leverage and influence points. Explain the Matrix formulation of the linear model Understand multiple regression, partial correlation, polynomial regression. Apply Analysis of Variance : multiple comparisons, two-way ANOVA, interactions Understand analysis of covariance. Overview of Generalized Linear Models including nonlinear regression, logistic regression and log-linear models.

Affective (Attitudes and Values)

Oral and written presentation skills Presenting technical information in a non scientifc format

Psychomotor (Physical Skills)

N/A

How the Module will be Taught and what will be the Learning Experiences of the Students:

A combination of theory and practice. The objective of the laboratory and tutorial sessions is to impart to students the theory and understanding underpinning the statistical output from statistical packages. Strong emphasis on the theory of residuals, influence points etc and how statistical packages enable us to identify and understand these issues.

Research Findings Incorporated in to the Syllabus (If Relevant):

Prime Texts:

Kleinbaum (2008) Applied Regresssion anlysis and other Multivariate Methods , Duxbury Press

Other Relevant Texts:

Douglas C Montgomery (2002) Regression Analysis , Wiley

Programme(s) in which this Module is Offered:

Semester(s) Module is Offered:

Module Leader:

Helen.Purtill@ul.ie