Objectives and competences

• Students acquire the knowledge and skills required to independently design empirical experiments. • Students develop the ability to independently perform statistical analyses of designed experiments. • Students gain an understanding of advanced statistical modeling methods. • Students develop computer and data-processing skills necessary for the application of advanced statistical techniques. • Students are able to interpret and critically evaluate results obtained from the applied statistical methods.

Content (Syllabus outline)

• design and analysis of experiments • multiple linear regression • logistic regression • analysis of one-factor experimental designs • analysis of multi-factor experimental designs • mixed models • multiple comparison tests • analysis of covariance • ANOVA for repeated measures • Nonparametric tests • concordance analysis • correlation and partial correlation • data analysis using statistical software and interpretation of results

Learning and teaching methods

lectures tutorials computer practical self-study

Intended learning outcomes - knowledge and understanding

Students should be able: • Understand the principles of statistical inference, experimental design, and data collection typical of the natural sciences. • Check the assumptions of statistical tests and apply the appropriate test based on data characteristics and the research hypothesis. • Perform the discussed statistical methods using statistical software and interpret the results. • Recognize common pitfalls in the application of statistical methodology. • Use statistical terminology in the English language.

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. • Field, Andy. (2018). Discovering statistics using IBM SPSS statistics, 5th ed. (5). Los Angeles: SAGE Edge. • Hadživuković, S. 1991. Statistički metodi, Poljoprivredni fakultet, Novi Sad. • Erčulj, V. ., & Šifrer, J. . (2020). Multivariatne metode v varstvoslovju s programom SPSS. Univerzitetna založba Univerze v Mariboru. https://doi.org/10.18690/ • KRANER ŠUMENJAK, Tadeja, SEM, Vilma. Parametrični in neparametrični pristopi za odkrivanje trenda v časovnih vrstah. Acta agriculturae Slovenica. [Tiskana izd.]. 2011, letn. 97, št. 3, str. 305-312.

Prerequisits

Student have an obligation to attend the tutorials (at least 80 %).