Biometry

Course Information
TitleΒΙΟΜΕΤΡΙΑ / Biometry
CodeGPP100
FacultyAgriculture, Forestry and Natural Environment
SchoolAgriculture
Cycle / Level2nd / Postgraduate, 3rd / Doctorate
Teaching PeriodWinter
CoordinatorAthanasios Mavromatis
CommonYes
StatusActive
Course ID600017380

Programme of Study: Prógramma metaptychiakṓn Spoudṓn Genetikī, Veltíōsī fytṓn kai Paragōgī Pollaplasiastikoý Ylikoý

Registered students: 11
OrientationAttendance TypeSemesterYearECTS
KORMOS - Ypochreōtiká & EpilogīsCompulsory Course118
Class Information
Academic Year2022 – 2023
Class PeriodWinter
Faculty Instructors
Instructors from Other Categories
Weekly Hours5
Total Hours65
Class ID
600218729
Course Type 2021
Specific Foundation
Course Type 2016-2020
  • Background
  • General Knowledge
  • Skills Development
Course Type 2011-2015
General 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
Required Courses
  • Ν006Υ STATISTICS
  • Ν523Ε AGRICULTURAL EXPERIMENTATION
General Prerequisites
Students should be familiar with the use of computers. Students should have attended courses on Statistics and Agricultural Experimentation.
Learning Outcomes
Upon completion of this course, students will be able to: 1) Explain fundamental ideas of Probability and Statistics and the theory behind the commonly used statistical techniques. 2) Apply suitable statistical techniques correctly for solving biological problems. 3) Analyze data using common statistical software and interpret outputs. 4) Prepare statistical reports and make presentations. 5) Communicate with a statistician.
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 Biometry. Descriptive and Inferential Statistics. Confidence intervals. Parametric and non-Parametric statistical tests. Agricultural experimentation and introduction to Analysis of Variance (ANOVA). Introduction to linear models (general/mixed). Methodology for setting up and statistically analysing data coming from simple experiments (Completely Randomized Design, Randomized Complete Block Design, Latin Square Design, Balanced Lattice Design). Methodology for setting up and statistically analysing data coming from factorial experiments (with crossed and/or nested factor structure). Statistical analysis of data coming from split plot designs and designs with repeated measures. Statistical analysis of data coming from designs combined over locations and/or years. Multiple linear and non linear regression. Introduction to Multivariate-Multidimensional Data Analysis.. Statistical Analyses for categorical data. Contingency tests. Analysis of biological data with statistical software.
Keywords
Experimental designs, Analysis of Variance, Linear and non linear Regression, Multivariate Data Analysis, Statistical Software
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
Web, Internet, Powerpoint, video, Excel, SPSS, educational software-tutorial, zoom,email
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures✓✓
Seminars✓✓
Laboratory Work✓✓✓
Reading Assigment✓✓
Tutorial✓✓✓
Project✓✓✓
Written assigments✓✓✓
Exams✓✓
Total
Student Assessment
Description
written exams (70%), Project (20%), Oral exams (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
Course Bibliography (Eudoxus)
1) Φωτιάδης, Ν. (1995). "Εισαγωγή στη Στατιστική για βιολογικές επιστήμες". Θεσσαλονίκη: University Studio Preee (Κωδικός Εύδοξος: 17225). 2) Φασούλας, Α. (2008). "Στοιχεία Πειραματικής Στατιστικής". Θεσσαλονίκη: Εκδόσεις Γαρταγάνη.(Κωδικός Εύδοξος: 1944).
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.
Last Update
17-11-2022