DESIGN OF EXPRETIMENTS AND DATA ANALYSIS USING STATISTICAL SOFTWARE

Course Information
TitleΣΧΕΔΙΑΣΜΟΣ ΠΕΙΡΑΜΑΤΩΝ ΚΑΙ ΑΝΑΛΥΣΗ ΔΕΔΟΜΕΝΩΝ ΜΕ ΣΤΑΤΙΣΤΙΚΑ ΠΑΚΕΤΑ / DESIGN OF EXPRETIMENTS AND DATA ANALYSIS USING STATISTICAL SOFTWARE
CodeAKA807
FacultyAgriculture, Forestry and Natural Environment
SchoolAgriculture
Cycle / Level2nd / Postgraduate, 3rd / Doctorate
Teaching PeriodWinter
CoordinatorGeorgios Menexes
CommonYes
StatusActive
Course ID600017442

Programme of Study: Prógramma metaptychiakṓn Spoudṓn Aeiforiká Geōrgiká Systīmata Paragōgīs kai Klimatikī Allagī

Registered students: 11
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses118
Class Information
Academic Year2022 – 2023
Class PeriodWinter
Faculty Instructors
Instructors from Other Categories
Weekly Hours5
Total Hours65
Class ID
600218720
Course Type 2021
Skills Development
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
  • GPP100 Biometry
  • AKA801 BIOMETRY I
General Prerequisites
Students should be familiar with the use of computers. Students should have attended courses on Statistics.
Learning Outcomes
Expected learning outcomes Upon successful completion of the course’s students will be able to: • To make decisions and design experiments from the field of Agricultural Sciences and more generally from the field of Biological Sciences. • To organize the data properly. • Properly encode the data. • To enter the data in PC properly. • To choose the appropriate method of statistical analysis. • To acquire practical skills and abilities in the use of statistical packages and in the performance of statistical analyzes. • Develop critical thinking about the biological significance and interpretation of the results of statistical analysis. • To communicate and collaborate with researchers from other scientific fields. • Learn to present and comment on the numerical and diagrammatic outputs of statistical methods. • To develop the ability to present the results of an experiment in a format suitable for disseminating the results to the scientific community. • To apply knowledge in practice.
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)
Content 1. Introduction to Descriptive and Inferential Statistics. 2. Introduction to Variance Analysis and Linear Regression. 3. Introduction to Experimentation. Introduction to Agricultural Experimentation. Purpose of Agricultural Experimentation. Experiments in the field, experiments in the greenhouse, experiments in the laboratory. Randomize, repeat, create groups. Experimental plans and strategies for the statistical processing of the respective data. 4. Exercise of designing experiments in practice. 5. Data Organization and Coding. 6. Data transformations. 7. Examples - Applications. Demonstration of the use of several statistical packages. 8. Learning to use MS Excel for data entry and statistical processing. 9. Learning to use the IBM SPSS statistical package for data entry and statistical processing. 10. Learning to use the R programming language for data entry and statistical processing. 11. Examples - statistical data processing applications using statistical packages. 12. Construction of statistical charts using statistical packages. 13. Presentation and commentary on the arithmetic and diagrammatic outputs of statistical methods. 14. Practical exercise of data analysis from agricultural experiments and in general from experiments of Biological Sciences.
Keywords
Design of experiments, Statistics, Statistical software
Educational Material Types
  • Notes
  • Slide presentations
  • Video lectures
  • Multimedia
  • Interactive excersises
  • 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 Powerpoint, video presentation, Excel, SPSS, educational material, zoom, email.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures✓✓
Seminars✓✓
Laboratory Work✓✓
Reading Assigment✓✓
Project✓✓
Written assigments✓✓
Exams✓✓✓
Total
Student Assessment
Description
Written, oral exams and projects.
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)
  • Labortatory Assignment (Formative, Summative)
Bibliography
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