Objectives and competences
• understanding of some advanced concepts in statistics
• ability to apply such knowledge and understanding to the solution of practical problems
• computational and data-processing skills
• competence in the planning experiments, collecting, analyzing and interpreting data
• ability to evaluate critically the empirical research
Content (Syllabus outline)
• design and analysis of experiments
• linear regression
• multiple linear regression
• analysis of one-factor experimental designs
• analysis of multi-factor experimental designs
• multiple comparisons tests
• analysis of covariance
• ANOVA for repeated measures
• Wilcoxon signed-ranks test
• Mann-Whitney U test
• Kruskal-Wallis one-way analysis of variance
• Friedman two-way analysis of variance
• concordance analysis
• correlation and partial correlation
• McNemar test
• analysis data with statistical software and interpretation the results
Learning and teaching methods
lectures
tutorials
computer practical
self-study
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
Students should be able:
• to comprehend the basic ideas of statistical inference, study design and data collection relevant to experiments that are typical for the natural sciences.
• to determine the appropriate parametric or nonparametric 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 carry out the analyses, with the help of SPSS (for not so complex procedures also without statistical program), interpret the results, and formulate conclusions in terms of the actual problem.
• to recognize pitfalls in using statistical methodology.
• to know statistical terminology in English.
Intended learning outcomes - transferable/key skills and other attributes
Readings
• Košmelj, K. 2004. Osnove analize kovariance, Acta agriculturae slovenica, 83 – 2. Dostopno na: http://aas.bf.uni-lj.si/november2004/13kosmelj.pdf
• Vasilj, Đ. 2000. Biometrika, Hrvatsko agronomsko društvo, Zagreb.
• Sheskin, D.J. 2000. Handbook of parametric and nonparametric statistical procedures, Chapman&Hall/CRC.
• Hadživuković, S. 1991. Statistički metodi, Poljoprivredni fakultet, Novi Sad.
Additional information on implementation and assessment Method (written or oral exam, coursework, project):
• written exam on practical and theoretical knowledge 50 %
• using SPSS (written exam) 50 %
The exam may be replaced with mid-term exams.
To pass this course, the students need both the practical and theoretical knowledge exam to have a grade of at least 6.
Student have an obligation to attend the tutorials (at least 80 %).
