Department of Marine Sciences

Multivariate Statistical Analysis - NOT AVAILABLE IN 2025-2026

(1) General

School:	Of the Environment
Academic Unit:	Department of Marine Sciences
Level of studies:	Undergraduate
Course Code:	191ΘΔ24Ε	Semester:	F
Course Title:	Multivariate Statistical Analysis - NOT AVAILABLE IN 2025-2026
Independent Teaching Activities		Weekly Teaching Hours	Credits
		Total credits	5
Course Type:	Specialised general knowledge
Prerequisite Courses:	Statistics
Language of Instruction and Examinations:	Greek
Is the course offered to Erasmus students:	No
Course Website (Url):	https://www.mar.aegean.gr/index.php?lang=en&lesson=1065&pg=3.1.1

(2) Learning Outcomes

Learning Outcomes

Explore and summarize multivariate data using graphical and numerical methods and techniques to uncover hidden information and patterns.
Describe properties of multivariate distributions such as multivariate norma
Use principal component analysis effectively for data exploration and data dimension reduction.
Discriminate between groups and classify new observations.
Find groupings and associations using cluster and correspondence analysis.
Use of statistical software packages effectively and efficiently.

General Competences

Search for, analysis and synthesis of data and information, with the use of the necessary technology
Decision-making
Working independently
Team work
Working in an international environment
Working in an interdisciplinary environment
Production of new research ideas
Respect for the natural environment
Production of free, creative and inductive thinking

(3) Syllabus

Introduction.
Multivariate descriptive statistics.
Multivariate distributions.
Hypothesis testing.
Multivariate analysis of variance.
Multiple linear regression
Principal component analysis.
Cluster analysis.
Discriminant analysis.
Correspondence analysis.

(4) Teaching and Learning Methods - Evaluation

Delivery:

Face-to-face

Use of Information and Communication Technology:

Use of statistical language (R) in teaching and in Labs. Use of platform open eclass, a complete Course Management System that supports Asynchronous eLearning Services. Instructor notes, homework.

Teaching Methods:

	Activity	Semester workload
	Lectures-Seminars	24
	Lectures	60
	Lectures	10
	Final exam	3
	Lectures	28
	*Course total*	125

Student Performance Evaluation:

Language of evaluation:

Greek.

Method of evaluation:

Final project: problem-solving through statistical software (20%)

End of semester exam: written problem solving (80%)

(5) Attached Bibliography

Καρλής Δ. 2005. Πολυμεταβλητή στατιστική ανάλυση. Σταμούλης
Bartholomew DJ, Steele F, Μουστάκη Ε, Galbraith JI. Ανάλυση πολυμεταβλητών δεδομένων για κοινωνικές επιστήμες. Κλειδάριθμος
Πετρίδης Δ. 2016. ΑΝΑΛΥΣΗ ΠΟΛΥΜΕΤΑΒΛΗΤΩΝ ΤΕΧΝΙΚΩΝ. (Ηλεκτρονικό βιβλίο) http://hdl.handle.net/11419/2126

Connection