BIOMETRY II

Course Information
TitleΒιομετρία ΙΙ / BIOMETRY II
CodeNAKA808Y
FacultyAgriculture, Forestry and Natural Environment
SchoolAgriculture
Cycle / Level2nd / Postgraduate, 3rd / Doctorate
Teaching PeriodWinter/Spring
CoordinatorGeorgios Menexes
CommonYes
StatusActive
Course ID600024882

Programme of Study: Sustainable Agricultural Systems and Climate Change

Registered students: 4
OrientationAttendance TypeSemesterYearECTS
KORMOSTheoretical Compulsory Courses218
Class Information
Academic Year2025 – 2026
Class PeriodSpring
Faculty Instructors
Weekly Hours5
Total Hours65
Class ID
600294723
Course Type 2021
Specific Foundation
Mode of Delivery
  • Face to face
  • Distance learning
Digital Course Content
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Instruction, Examination)
Prerequisites
General Prerequisites
Students should be familiar with the use of computers. Students should have attended courses on Statistics, Agricultural Experimentation and Biometry I.
Learning Outcomes
At the end of the course, the students will: • Develop abstract thinking. • Understand the complex relationships between variables. • Think and will treat Biological phenomena as a system. • Can recognize and define the parameters of the phenomenon under consideration. • Learn to organize data appropriately. • Learn to encode data appropriately. • Learn to enter data into a computer. • Learn to choose the appropriate method of multivariate analysis. • Recognize the differences but also the complementarity of the methods. • Apply knowledge in practice. • Learn to use Statistical Programs. • Learn to communicate with researchers from other scientific specialties. • Learn to understand, present and comment on numerical and graphical outputs of methods.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Work in an international context
  • Work in an interdisciplinary team
  • Generate new research ideas
  • Design and manage projects
  • Respect natural environment
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Introduction to Multivariate-Multidimensional Data Analysis. Basic principles and new methodological approaches. Schools of Data Analysis. Biological phenomena as statistical systems. What lies ahead? Introduction to Mixed Linear Models and to Multivariate Data Analysis. Methodological frame and requirements. Statistical Analysis of Variance (ANOVA) of data coming from agricultural experiments combined over locations and/or years. Study of the interaction between factors and/or the environment. Comparisons of means. Requirements for Combined Analysis of Variance. Requirements statistical checking. Applications, presentation and reporting of results. Introduction to Factor Analysis. Principal Component Analysis (PCA) and variants of PCA. Objectives of the method. Methodological frame and requirements. Applications, presentation and reporting of results. Reliability analysis. Methodological frame and requirements. Applications, presentation and reporting of results. Introduction to Multivariate Analysis of Variance (MANOVA). Objectives of the method. Methodological frame and requirements. Applications, presentation and reporting of results. Hierarchical Cluster Analysis (Classification). Objectives of the method. Methodological frame and requirements. Applications, presentation and reporting of results. Discriminant Analysis. Objectives of the method. Methodological frame and requirements. Applications, presentation and reporting of results. Logistic Regression. Multivariate Data Analysis with statistical software.
Keywords
Multivariate and Multidimensional Data Analysis
Educational Material Types
  • Notes
  • Slide presentations
  • Video lectures
  • Multimedia
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
  • Use of ICT in Student Assessment
Description
Use of Web, Internet, Powerpoint, video, Excel, SPSS, educational software-tutorial, zoom, email.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures65✓✓
Seminars20✓✓
Laboratory Work20✓✓✓
Reading Assigment25✓✓
Tutorial25✓✓✓
Project20✓✓✓
Written assigments20✓✓✓
Exams5✓✓✓
Total200
Student Assessment
Description
Written exams (70%), Project (20%), Oral exam (10%)
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Formative, Summative)
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Exam with Extended Answer Questions (Formative, Summative)
  • Written Assignment (Formative, Summative)
  • Oral Exams (Formative, Summative)
  • Performance / Staging (Formative, Summative)
  • Written Exam with Problem Solving (Formative, Summative)
  • Report (Formative, Summative)
Bibliography
Additional bibliography for study
1) Steel, R., Torrie, J. & Dickey, D. (1997). Principles and Procedures of Statistics: A Biometrical Approach. Third Edition. Singapore: McGraw-Hill Book Company. 2) Gomez, K. & Gomez, A. (1984). Statistical Procedures for Agricultural Research. Singapore: John Willey & Sons, Inc. 3) Zar, J. (1996). Biostatistical Analysis. New Jersey: Prentice-Hall International, Inc. 4) Μενεξές, Γ. & Οικονόμου, Α. (2002). Σφάλματα και Παρανοήσεις στους Στατιστικούς Ελέγχους Υποθέσεων: Υπέρβαση μέσω της Ανάλυσης Δεδομένων. Τετράδια Ανάλυσης Δεδομένων-Data Analysis Bulletin, 2, 52-64. 5) Μενεξές, Γ. (2007). Μια Δομημένη Προσέγγιση στην Πολυμεταβλητή Στατιστική Ανάλυση Βιολογικών, Περιβαλλοντικών, Κοινωνικών και Οικονομικών Δεδομένων. Στο Φυσικοί Πόροι, Περιβάλλον και Ανάπτυξη (σσ. 519-534). Επιμέλεια: Γ. Αραμπατζής και Σ. Πολύζος. Θεσσαλονίκη: Εκδόσεις Τζιόλα. 6) Μενεξές, Γ. (2013). Οδηγός Ανάλυσης Παραλλακτικότητας Δεδομένων Γεωργικών Πειραμάτων με Στατιστικά Πακέτα. Εκπαιδευτικές Σημειώσεις. 7) Hair, J., Anderson, R., Tatham, R. & Black, W. (1995). Multivariate Data Analysis With Readings. New Jersey: Prentice-Hall International, Inc.
Last Update
01-09-2024