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Module Code - Title:

MA4128 - ADVANCED DATA MODELLING

Year Last Offered:

2022/3

Hours Per Week:

Lecture

3

Lab

0

Tutorial

0

Other

2

Private

4

Credits

6

Grading Type:

N

Prerequisite Modules:

EC4307
MA4125

Rationale and Purpose of the Module:

To ground the students in Applied Multivariate Analysis. The module serves business and mathematics students. It introduces the mathematical statistical ideas behind Principal Component Analysis, Factor Analysis, Cluster Analysis, Discrimination Function and the Multiple Linear Logistic function. The students learn how to implement these techniques in Minitab to become competent in the analysis of a wide variety of multivariate data structures.

Syllabus:

Principal Component Analysis, Cluster Analysis, Discrimination Function and the Multiple Linear Logistic function and Factor Analysis are introduced in this order. From the outset the Minitab (Statistical Package) is introduced. Different types of multivariate data structures are introduced. The analyses appropriate to each type of data structure are deduced from general principles and their implementation in Minitab described. Many different data structures are considered. Emphasis is placed on the integration of the different methods of analysis available in order to achieve an effective interpretation and simple summary of the multivariate data. Report writing, communicating the interpretation to non-technical business managers, is taught.

Learning Outcomes:

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

On successful completion of this module the student should be able to: 1. Utilise the software package Minitab for applied Multivariate statistical analysis 2. Recognise the underlying structure of the various multivariate data sets covered in the course. 3. Analyse data using the following key methods: Principal Component Analysis, Factor Analysis, Cluster Analysis, Discriminant Function Analysis and Regression Analysis, 4. Conduct exploratory data analyses using appropriate combinations of these techniques to reveal where possible the (simplest) structure of the multivariate data set. 5. Recognise the conditions under which the results are valid. 6. Interpret the findings from these methods and write lucid and succinct reports for managers in an industrial or commercial setting ( eg on market segmentation).

Affective (Attitudes and Values)

Develop sharp critical appraisal skills. Judge which statistical methods of analysis are appropriate Defend any decisions made Challenge incorrect applications of methods

Psychomotor (Physical Skills)

N/A

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

The module is taught by lectures and computer labs. A 100 page specially written course booklet is available. Minitab is available outside course time in various labs but is not available for home use.

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

Some Research Data Sets are made available to the students in labs and Research Data Sets are used in exam questions all of which are unseen.

Prime Texts:

Morrison (1967) Multivariate statistical methods , McGraw-Hill
Cooley & Lohnes (1971) Multivariate Data Analysis , Wiley
Sharma (1996) Applied Multivariate Techniques, , Wiley

Other Relevant Texts:

Programme(s) in which this Module is Offered:

Semester - Year to be First Offered:

Spring - 08/09

Module Leader:

Norma.Bargary@ul.ie