Design of Experiments (152084)

Course coordinator

Course description

Comparative scientific studies are based on experiments, carried out in order to test the research hypothesis. Therefore, adequate planning, performing, and analysis of experimental data are prerequisite for the success of the scientific work. This module is conceived to enable students to get acquainted with basic terms in experimentation, and to introduce them to various types of experimental design. Furthermore, students will learn the basic principles of planning and analysis of experimental data, as well as specifics of specialized designs. Particular emphasis will be given on the choice of the appropriate design, one that should adequately accommodate research hypothesis, enable the most efficient statistical analysis for the most informative interpretation of the results.
Prerequisite for successful following of the courses is proficiency in statistical methods included in a basic statistics course: t-test, analysis of variance, regression.

Type of course

ECTS: 3.00

E-learning: L1

Teaching hours: 30
Lectures: 15
Practicum: 11
Seminar: 4

Lecturer
Associate teacher for exercises
Associate teacher for seminars
Grading

Sufficient (2): 60-69 %
Good (3): 70-79 %
Very good (4): 80-89 %
Excellent (5): 90-100%

General competencies

Students receive the necessary theoretical and practical knowledge about the design of experiments.

Types of instruction

  • Lectures
    Designed to provide necessary theoretical background.
  • Practicum
    The tutorials consist of a statistical analysis of the examples using the software package R, and interpretation of the results. They are conducted in groups of 10-15 students.
  • Field work
    Visit to the field trials of the Department of Plant Breeding, Genetics and Biometrics.
  • Seminars
    Three "case studies" - a description, statistical analysis and interpretation of results in the form of scientific paper.

Learning outcomes

Learning outcome Evaluation methods
understand the basic principles of experimentation, written exam
describe the various characteristics of the most commonly used experimental plans, written exam
select the optimal design for the planning of experiments, seminars
analyze the collected data using an appropriate model for the statistical analysis of experimental data, seminars
present and interpret the results of statistical data analysis, seminars
compare the efficiency of different designs, seminars, written exam
recommend the optimal design and appropriate statistical model suited to the given objectives of research seminars, written exam

Working methods

Teachers' obligations

All teaching materials are organized and to complement appropriate teaching units. They are ready to became available through the Merlin – the system for e-learning, which includes forum for communication with students, calendar of major events, information related to the course, on-course assignments, instructions for the use of teaching materials, evaluation methods for seminars and written examinations.

Students' obligations

Attending lectures, tutorials and seminars is required, and the students should participate in e-learning. It is expected that the students will be able to log into the Merlin system for e-learning Merlin during the first two weeks of classes, and use it to get access to lecture presentations, examples of solved problems from seminars and other materials. Prerequisite for the final exam is regular attending to lectures and tutorials, as well as the successful completion of seminars.

Methods of grading

Evaluation elements Maximum points or Share in evaluation Grade rating scale Grade Direct teaching hours Total number of average student workload ECTS
Pohađanje nastave (predavanja +vježbe) 54 54 2
Aktivno sudjelovanje na nastavi 10 % 0 30 0,5
Seminarski rad (S) (priprema+prezentacija) 10 % 6 16 1
Parcijalni ispit 1 (PI1) 20 % <60 %
60-70 %
71-80 %
81-90 %
91-100 %
Insufficient (1)
Sufficient (2)
Good (3)
Very good (4)
Excellent (5)
0 20 0,5
Parcijalni ispit 2 (PI2) 20 % <60 %
60-70 %
71-80 %
81-90 %
91-100 %
Insufficient (1)
Sufficient (2)
Good (3)
Very good (4)
Excellent (5)
0 20 0,5
Parcijalni ispit 3 (PI3) 20 % <60 %
60-70 %
71-80 %
81-90 %
91-100 %
Insufficient (1)
Sufficient (2)
Good (3)
Very good (4)
Excellent (5)
0 20 0,5
Usmeni ispit (UI) 20 % <60 %
60-70 %
71-80 %
81-90 %
91-100 %
Insufficient (1)
Sufficient (2)
Good (3)
Very good (4)
Excellent (5)
0 20 1
UKUPNO 100 % (S+PI1+PI2+PI3+UI)/5 60 180 6
Evaluation elements Description Deadline Recoupment
Pohađanje nastave (predavanja +vježbe) Na nastavi se redovito bilježe nazočni studenti (na početku i kraju bloka) i prati sposobnost discipliniranog i aktivnog praćenja nastave Može se opravdati izostanak do 20% predavanja, 15% vježbi i 15% seminara (čl. 12 Pravilnika o studiranju na AFZ-u) Semestar (60 sati izravne nastave) Moguća putem samostalnog zadatka (1 ECTS)
Aktivno sudjelovanje na nastavi Aktivno sudjelovanje u nastavi korigira ocjenu naviše. Studenti se potiču sudjelovati u raspravama, prezentaciji ideja i problemskih rješenja, argumentiranju mišljenja i stavova. Prati se usvajanje teorijskih i činjeničnih znanja, prezentacijskih i komunikacijskih vještina, kritičkog mišljenja, timskog rada i društvene odgovornosti. Prati se sposobnost samostalnog izvođenja laboratorijskih vježbi. Zapažena aktivnost na satu bilježi se u studentskoj evidenciji (+), što omogućuje korekciju konačne ocjene naviše (++) ili beneficiju na usmenom ispitu (+++). Kontinuirano tijekom izvođenja nastave Moguća putem samostalnog zadatka (1 ECTS)
Seminarski rad (S) (priprema+prezentacija) Seminarski rad na početku semestra zadužuje svaki student pojedinačno. Pisani rad se predaje asistentu na pregled najmanje tjedan dana prije izlaganja. Korigirani rad predaje se pri izlaganju. Izlaganja seminarskih radova počinju u 13. tjednu nastave u semestru prema dogovorenom rasporedu. Studenti samostalno izlažu seminare i ocjenjuju prezentacijske vještine, analitičnost i sposobnost zaključivanja (sinteze). Struktura i sadržaj pisanog rada 50% Uvjerljivost prezentacije 50% 13. tjedan 14.tjedan Moguća putem samostalnog zadatka (1 ECTS)
Parcijalni ispit 1 (PI1) Obuhvaća prvi programski dio modula: teorijske osnove hlađenja koji obrađuje područja termodinamike, suvremene izolacijske materijale, rashladne medije s posebnim naglasakom na ekološka rashladna sredstva. Pitanja iz teorijskog dijela su otvorenog tipa i ispituju poznavanje i razumijevanje činjenica. 4.tjedan
Parcijalni ispit 2 (PI2) Obuhvaća drugi programski dio modula: rashladni sustavi u poljoprivredi koji obrađuje područja hlađenja i održavanja poljoprivrednih proizvoda (uvjeti, načini, postrojenja, i dr.) po grupama; zamrzavanja - konzerviranja niskim temperaturama, način, brzinu i vremena zamrzavanja, utjecajne čimbenike. Pitanja iz teorijskog dijela su otvorenog tipa i ispituju poznavanje i razumijevanje činjenica. 9.tjedan
Parcijalni ispit 3 (PI3) Obuhvaća treći programski dio modula: tehnička sredstva i objekti za hlađenje koji obrađuje područja objekata – hladionica, rashladna postrojenja, tehnologije hlađenja, kao i o transportna sredstva i hlađenje robe tijekom transportnog procesa. Projektiranje rashladnih postrojenja, postavljanje i proračun osnovnih parametara za tehnološki zadatak u izgradnji rashladnih kapaciteta, odabira hladionice i rashladnog medija. Pitanja iz teorijskog dijela su otvorenog tipa i ispituju poznavanje i razumijevanje činjenica. 15.tjedan
Nadoknada (samostalni zadatak) Ukoliko student ne ostvari nužna 3 ECTS boda kao preduvjet izlaska na usmeni ispit, jedan bod je moguće nadoknaditi dodatnim samostalnim zadatkom, npr: prijevod stručnog teksta s engleskog jezika i izlaganje pred nastavnikom, prikaz članka ili knjige, projektna ideja i sl. Tijekom ispitnih rokova, prije usmenog ispita
Usmeni ispit (UI) Usmeni ispit se sastoji od tri, eventualno dva pitanja (+++), ovisno o prethodnoj aktivnosti studenta . Testira se usvojenost teorije i činjenica, analitičnost, kritičko mišljenje, kreativnost i društvena odgovornost. Ispitni rokovi

Weekly class schedule

  1. Introduction to experimentation L - Principles of experimentation; introduction to basic terms in experimentation – treatments, plots, replication, randomization.
  2. Introduction to experimentation L, P - Linear models, least squares, treatment comparisons/multiple comparisons, residuals. Introduction to R – software environment for statistical computing.
  3. Introduction to experimentation P, F - Model building in R. Visit to experimental field of Department of Plant Breeding, Genetics and Biometrics.
  4. Multifactorial experiments L - Analysis of multifactorial experiments – treatment structure, factors, levels, additivity, interaction, fixed and random effects, hierarchical and crossclassified designs.
  5. Multifactorial experiments P - Modelling in R
  6. Complete block designs L - Design structure, blocks/replicates, experimental error control, design efficiency – completely randomized design, randomized complete block design
  7. Complete block designs P - Modelling in R
  8. Complete block designs L - Latinized designs – Latin square
  9. Complete block designs S - Modelling in R – "case study"
  10. Incomplete block designs L - Block size, experimental error control, treatment comparisons, recovery of inter-block information, design efficiency – alpha design, row-column design.
  11. Incomplete block designs P - Modelling in R
  12. Split-plot designs L - Unequal plot size, multiple errors, treatment comparisons, multifactorial designs – split-plot
  13. Split-plot designs P - Modelling in R
  14. Some advanced designs L - Repeated measures, analysis of covariance, «crossover» design
  15. Exam

Obligatory literature

  1. Kuehl, R.O., 2000. Design of Experiments. Duxbury, Pacific Grove

Recommended literature

  1. Gomez, KA, and Gomez, AA, 1984. Statistical Procedures for Agricultural Research. John Wiley & Sons

Similar course at related universities

  • Experimental Design, BOKU
  • Advanced Statistics course: Design of Experiments, Wageningen UR
  • Experimental Trials in Agriculture, University of Hohenheim
  • Linear mixed models and experimental design with applications to agricultural field experiments, Swedish University of Agricultural Sciences

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