1. komponenta

Lecture type	Total
Lectures	30
Practicum	45

* Load is given in academic hour (1 academic hour = 45 minutes)

This course is designed to strengthen students? competencies in understanding and conducting biological research. Viewing biological data as variables enables not only appropriate analyses and hypothesis testing but also proper research design. Through numerous biological examples and smaller datasets, students will gain practical knowledge and understanding of data collection and analysis. Exercises are conducted in R, but sufficient time and small-group settings ensure that students can master the material regardless of their prior knowledge or computer skills.
Course objectives
The objective of the course is to equip students with the ability to independently perform basic descriptive and statistical analyses of biological data in the R programming environment. The course contributes to the development of competencies in formulating and testing hypotheses, designing experiments and collecting data in biological research, identifying key types of variables in such research, and selecting appropriate types of descriptive and statistical analyses for collected data. Another goal is to enable students to produce basic graphical and tabular representations of collected data and analysis results, as well as to interpret the biological significance of the obtained results.

Course content
Types of biological data and biological variables (experiments, sampling)
Proportions and frequencies, probability in biological research
Data distributions in biological research
Descriptive analyses and graphical visualization of biological data, measures of central tendency and measures of variability
Hypothesis testing in biological research, null hypothesis, sample and population, statistical and biological significance
Parametric statistics of biological data ? assumptions, tests, data transformations
Overview of basic statistical analyses of categorical and numerical variables in biological research
Regression and correlation, linear models of biological data
Experimental design in biological research, replicates, test power, accuracy, and precision
Nonparametric statistics of biological data
Multivariate analyses of biological data
Exercises follow the teaching units and are carried out in the R environment.

1. The Analysis of Biological Data, Michael Whitlock and Dolph Schluter, Roberts and Company Publishers.
2. Interna scripta, Štambuk, Safner, Karlić, Kanjer.
3. Dizajn eksperimenata "S primjenom u poljoprivredi", Jerko Gunjača, Sveučilište u Zagrebu Agronomski fakultet i Znanstveni centar izvrsnosti za bioraznolikost i molekularno oplemenivanje bilja.

MODUL: BIOLOŠKA ZAŠTITA OKOLIŠA - Regular study - Environmental Sciences

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