Objectives and competences
• understanding basic concepts of statistics
• apply such knowledge and understand the solution of practical problems
• computational and data-processing skills
• competence in planning experiments, collecting, analyzing and interpreting data
• ability to critically evaluate empirical research
Content (Syllabus outline)
• basic definitions – statistical variables, representing statistical data, indices
• frequency distribution – classification data, graphical representation
• quantiles and box-plot
• measure of central tendency, measure of variability, measure of skewness and kurtosis
• probability distributions and distributions of sample statistics
• sampling and experimental designs in agriculture
• estimation of population mean, one sample t-test, paired samples t-test, independent samples t-test, testing proportions, analysis of variance, multiple comparisons tests
• regression and correlation
• time series (components of time series)
• chi-square test
• analysing the data with statistical package SPSS
Learning and teaching methods
lectures
tutorials
computer practicals
self-study
Intended learning outcomes - knowledge and understanding
Students should be able:
• to apply fundamental concepts in exploratory data analysis,
• to comprehend the basic ideas of statistical inference, study design and data collection relevant to experiments that are typical for natural sciences,
• to determine the appropriate statistical test, given the research question and the type of data,
• to carry out the needed analyses for the discussed situations and interpret the results in terms of the problem,
• to perform independently data analysis with the computer package SPSS and interpret the SPSS output,
• to recognize pitfalls in using statistical methodology,
• and to know statistical terminology in English.
Intended learning outcomes - transferable/key skills and other attributes
Readings
• T. Kraner Šumenjak, V. Sem, Tables, formulas, and exercises with key for biometrics, vir je dostopen: http://press.um.si/index.php/ump/catalog/book/336
• D. Horvat in M. Ivezić, Biometrika o poljoprivredi, Osijek 2005.
• K. Košmelj, Uporabna statistika, Biotehnična fakulteta, Ljubljana 2001.
• Đ. Vasilj, Biometrika, Hrvatsko agronomsko društvo, Zagreb 2000.
• S. Hadživuković, Statistički metodi, Poljoprivredni fakultet, Novi Sad 1991.
Additional information on implementation and assessment Method (written or oral exam, coursework, project):
• written exam on practical and theoretical knowledge
The exam may be replaced with mid-term exams.
100