Applied Multivariate Analysis (ΣΕΕ10): Διαφορά μεταξύ των αναθεωρήσεων

Από Wiki Τμήματος Μαθηματικών
Χωρίς σύνοψη επεξεργασίας
Χωρίς σύνοψη επεξεργασίας
 
Γραμμή 1: Γραμμή 1:
* [[Εφαρμοσμένη Πολυδιάστατη Ανάλυση (ΣEE10)|Ελληνική Έκδοση]]
* [[Εφαρμοσμένη Πολυδιάστατη Ανάλυση (ΣEE10)|Ελληνική Έκδοση]]
{{Course-Graduate-Top-EN}}
{{Course-Graduate-Top-EN}}
{{Menu-OnAllPages-EN}}


=== General ===
=== General ===

Τελευταία αναθεώρηση της 17:39, 15 Ιουνίου 2023

General

School School of Science
Academic Unit Department of Mathematics
Level of Studies Graduate
Course Code ΣΣΕ10
Semester 2
Course Title

Applied Multivariate Analysis

Independent Teaching Activities Lectures-Laboratory (Weekly Teaching Hours: 3, Credits: 7.5)
Course Type

Specialized general knowledge

Prerequisite Courses -
Language of Instruction and Examinations Greek
Is the Course Offered to Erasmus Students

Yes (in English, reading Course)

Course Website (URL) See eCourse, the Learning Management System maintained by the University of Ioannina.

Learning Outcomes

Learning outcomes

Students completing this course should be able to:

  • Apply basic multivariate methods of statistical analysis
  • Choose the appropriate method of multivariate data analysis for a given multivariate data set, depending on the objectives of the study
  • Implement methods of dimension reduction
  • Interpret the results of multivariate data analyses.
  • Carry out multivariate data analysis through a statistical software (SPSS, SAS, Matlab, R)
General Competences
  • Working independently
  • Decision-making
  • Production of free, creative and inductive thinking
  • Project planning and management
  • Criticism and self-criticism

Syllabus

This course covers the following topics with applications mainly with SPSS and R: Graphical display of multivariate data, Data reduction techniques, Principal component analysis, Factor analysis, Canonical correlation analysis, Cluster analysis, Discriminant analysis, MANOVA, Repeated measurement analysis, Neural Networks. Applications with SPSS and R.

Teaching and Learning Methods - Evaluation

Delivery Classroom (face-to-face)
Use of Information and Communications Technology

Use of ICT in communication with students

Teaching Methods
Activity Semester Workload
Lectures 39
Working independently 78
Exercises-Homework 70.5
Course total 187.5
Student Performance Evaluation

Final written exam in Greek (in case of Erasmus students in English).

Attached Bibliography

  • Anderson, T.W. (2003). An Introduction to Multivariate Statistical Methods, 3nd ed., Wiley.
  • Giri, N.J. (2004). Multivariate Statistical Analysis, 2nd edition, Marcel Dekker, New York.
  • Johnson, R. A. and Wichern, D.W. (1998). Applied Multivariate Statistical Analysis, 4th ed. Prentice Hall.
  • Timm, N. H. (2002). Applied Multivariate Analysis. Springer.
  • Καρλής, Δ. (2005).Πολυμεταβλητή Στατιστική Ανάλυση. Εκδόσεις Σταμούλη
  • [Περιοδικό / Journal] Journal of Multivariate Analysis