Applied data analysis and statistics

Basic knowledge of statistics in the field of master studies.

przedmiot obligatoryjny

Contact hours with teacher:

- participation in workshop - 20 hrs

- consultations - 10 hrs

Self-study hours:

- preparation of the project - 20 hrs

Altogether: 50 hrs (2 ECTS)

Student

W1: defines a task or problem in the field of science or art and selects appropriate statistical methods to solve them

W2: knows classical and multivariate statistical techniques

Student

U1: applies advanced knowledge in the field of statistics to the research data and is able to use computer software for calculation and presentation of the results

U2: is able to use a foreign language to communicate in accordance with the requirements of B2 ESOKJ

U3: has the ability to present results in English, as well as prepare a report in English

Student

K1: demonstrates the ability to use statistical and multivariate methods to develop and present results

K2: can work in a team, both by directing and coordinating the team's activities and by performing assigned tasks

Expository teaching methods:

discussion, presentation, video/computer, pointer, banners image

- pokaz

- wykład informacyjny (konwencjonalny)
- wykład problemowy

- projektu
- studium przypadku

- metody oparte na współpracy
- metody służące prezentacji treści
- metody wymiany i dyskusji

Summary of basic statistical and multivariate methods and applications of statistical functions for analysis and research planning.

Classes include the application of statistical methods in science and/or art: description of data types, description of relationships using basic linear and nonlinear functions, parametric and nonparametric significance tests, linear regression, classification, and indirect and direct ordination methods. Students are welcome to work with their data and solve research questions of any branch of science or art. List of topics:

1. Basics of descriptive statistics – mean, median, modal, standard deviation, standard error, coefficient of variation

2. Testing simple hypothesis I – parametric tests: t-test, F-test, Chi2- test, one- and two-way ANOVA

3. Testing simple hypothesis II – non-parametric tests: Man-Whitney, Wilcoxon, Kruskal-Wallis with Dunn post-hoc comparison, PERMANOVA

4. On correlation and regression – basics of linear correlation and regression

5. Basics of classification methods – hierarchical agglomerative clustering, non-hierarchical k-means clustering

6. Basics of ordination methods – indirect (PCA, CA, DCA, and NMDS) and direct ordination (CCA, RDA)

Zar J.H. 2010. Biostatistical analysis. Prentice Hall, Pearson International Edition.

Rosner, Bernard (Bernard A.). (2011). Fundamentals of biostatistics. Boston: Brooks/Cole, Cengage Learning.

Gauch H.G., 1982. Multivariate analysis in community ecology. Cambridge University Press, Cambridge.

Jongman R.H.G., ter Braak C.J.F., van Tongeren D.F.R., (eds). 1995. Data analysis in community and landscape ecology. Pudoc, Wageningen.

ter Braak C.J.F., 1996. Unimodal models to relate species to environment. DLO-Agrocultura;l Mathematics Group, Wageningen, the Netherlands.

ter Braak, C. J. F., & Smilauer, P. (2012). Canoco reference manual and user's guide: software for ordination, version 5.0. Microcomputer Power.

Šmilauer, P., & Lepš, J. (2014). Multivariate Analysis of Ecological Data using CANOCO 5 (2nd ed.). Cambridge: Cambridge University Press. doi:10.1017/CBO9781139627061

Lepš, J., & Šmilauer, P. (2020). Biostatistics with R: An Introductory Guide for Field Biologists. Cambridge: Cambridge University Press. doi:10.1017/9781108616041

Hammer, Ø., Harper, D.A.T., and P. D. Ryan, 2001. PAST: Paleontological Statistics Software Package for Education and Data Analysis. Palaeontologia Electronica 4(1): 9pp.

Laboratory – project in groups 61-68% satisfactory, 69-76% satisfactory plus, 77-84 % good, 85- 92% good plus, 93-100% very good W1, W2, U1, U2, U3, K1, K2

not applicable