AGRICULTURAL EXPERIMENTATION
| Title | ΓΕΩΡΓΙΚΟΣ ΠΕΙΡΑΜΑΤΙΣΜΟΣ / AGRICULTURAL EXPERIMENTATION |
| Code | Ν523Ε |
| Faculty | Agriculture, Forestry and Natural Environment |
| School | Agriculture |
| Cycle / Level | 1st / Undergraduate, 3rd / Doctorate |
| Teaching Period | Winter |
| Common | Yes |
| Status | Active |
| Course ID | 420001976 |
Programme of Study: PPS Geōponías (2019-sīmera)
Registered students: 20
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses belonging to the other | Winter | - | 5 |
| FYTIKĪS PARAGŌGĪS | Elective Course belonging to the selected specialization (Elective Specialization Course) | Winter | - | 5 |
| Academic Year | 2022 – 2023 |
| Class Period | Winter |
| Faculty Instructors |
|
| Instructors from Other Categories |
|
| Weekly Hours | 4 |
| Class ID | 600210502
|
Class Schedule
| Building | Γεωπονίας & Δασολογίας |
| Floor | Όροφος 5 |
| Hall | ΕΡΓΑΣΤΗΡΙΟ ΣΗΡΟΤΡΟΦΙΑΣ ΜΕΛΙΣΣΟΚΟΜΙΑΣ (62) (39) |
| Calendar | Δευτέρα 11:00 έως 14:00 |
Course Type 2021
Specific Foundation
Course Type 2016-2020
- Background
- Scientific Area
- Skills Development
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600210502
- eLearning:
- OpenCourses (eClass): https://eclass.auth.gr/courses/OCRS144/
- Other: http://users.auth.gr/gmenexes
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Prerequisites
Required Courses
- Ν003Υ INFORMATICS
- Ν005Υ MATHEMATICS
- Ν006Υ STATISTICS
General Prerequisites
Students should be familiar with the use of computers. Students should have attended courses on Statistics.
Learning Outcomes
Upon completion of this course, students will have:
1) knowledge of the decisions that need to be made when designing and setting up an experiment;
2) knowledge of the options available to them for statistical analysis;
3) practical skills for undertaking those analyses;
4) Critical thinking skills relative to the biological meaning and interpretation of the results of the statistical analyses;
5) the ability to present their findings in a style appropriate to the scientific literature.
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 agricultural experimentation. Aims of experiments in agriculture. Field experiments, greenhouse experiments, lab experiments. Methodology for setting up agricultural experiments. Randomization, replication, blocking. Intoduction to Analysis of Variance (ANOVA). Statistical hypothesis testing procedures. Experimental error. Completely Randomized Design. Comparisons of means. Randomized Complete Block Design. Latin Square Design. Factorial experiments. Main effects and interactions between factors. Introduction to Linear Regression. Data transformations. Examples and applications. Demonstration of statistical softwares. Practice for designing field experiments.
Keywords
Experimental designs, Analysis of Variance, Linear Regression
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 Communication with Students
- Use of ICT in Student Assessment
Description
Powerpoint, video, Excel, SPSS, educational software-tutorial, email.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 68 | ✓ | ✓ | ||
| Seminars | 30 | ✓ | ✓ | ||
| Tutorial | 34 | ✓ | ✓ | ✓ | |
| Exams | 8 | ✓ | ✓ | ||
| Total | 140 |
Student Assessment
Description
100% written exams
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Exam with Short Answer Questions (Summative)
- Written Exam with Extended Answer Questions (Summative)
- Written Exam with Problem Solving (Summative)
Bibliography
Course Bibliography (Eudoxus)
1)Φασούλας, Α. (2008). "Στοιχεία Πειραματικής Στατιστικής". Θεσσαλονίκη: Εκδόσεις Γαρταγάνη. (Κωδικός Εύδοξος: 1944).
Additional bibliography for study
1) 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.
2) 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.
3) Μενεξές, Γ. (2013). Οδηγός Ανάλυσης Παραλλακτικότητας Δεδομένων Γεωργικών Πειραμάτων με Στατιστικά Πακέτα. Εκπαιδευτικές Σημειώσεις.
Last Update
17-11-2022
