Τμήμα Ωκεανογραφίας και Θαλασσίων Βιοεπιστημών

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Multivariate Statistical Analysis - NOT AVAILABLE IN 2025-2026
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
Credits
5
Course Type:
Specialised general knowledge
Prerequisite Courses:
Statistics
Language of Instruction and Examinations:
Greek
Is the course offered to Erasmus students:
No
  • (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
Lectures
28
Final exam
3
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