546-0169/01 – Basics of Environmental Data Processing (ZZED)

Gurantor departmentDepartment of Environmental EngineeringCredits3
Subject guarantorIng. Hana Švehláková, Ph.D.Subject version guarantorIng. Hana Švehláková, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Year2Semesterwinter
Study languageCzech
Year of introduction2021/2022Year of cancellation
Intended for the facultiesHGFIntended for study typesBachelor
Instruction secured by
LoginNameTuitorTeacher giving lectures
MOT127 Mgr. Oldřich Motyka, Ph.D.
KRE71 Ing. Hana Švehláková, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Graded credit 0+2
Part-time Graded credit 0+8

Subject aims expressed by acquired skills and competences

Students will be able to analyze and evaluate quantitative, semi-quantitative and qualitative biological data. They will be able to correctly describe and visualize these data using the basic characteristics of descriptive statistics, identify and verify erroneous and outliers and the distribution from which the biological data come. They will also be able to formulate a statistical hypothesis, use appropriate test statistics and correctly interpret the results. They will also gain basic skills in correlation and regression analysis of biological data.

Teaching methods

Tutorials

Summary

Students will be acquainted with the basic methods of preparation of a sampling plan, collection, standartization/tranformation and evaluation of biological data. They will learn to use and interpret basic statistical methods with regard to the specifics of biological data. They will also get acquainted with the basics of biostatistical models and statistical evaluation of diversity. Working with data will be using MS Excel tools and R program and its libraries.

Compulsory literature:

SOKAL, R., R.,ROHLF, F.J. Biometry : the principles and practice of statistics in biological research. 4th ed. New York, N.Y.: W.H. Freeman and Company, 2012. xix, 937. ISBN 9780716786047 ZAR, J., H. Biostatistical analysis. Fifth edition. Uttar Pradesh, India: Pearson India Education Services, 2014. 756 stran. ISBN 9789332536678

Recommended literature:

CRAWLEY, M., J. Statistics : an introduction using R. Chichester: John Wiley & Sons, 2005. xiii, 327. ISBN 0470022973 PETRIE, A., WATSON, P. Statistics for Veterinary and Animal Science. Wiley-Blackwell; 2nd ed, 2006

Way of continuous check of knowledge in the course of semester

Active participation in exercises. Processing and submission of assigned protocols. Semester work.

E-learning

Submission of assigned protocols. Semester work.

Other requirements

participation in exercises project - data evaluation credit test

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

1. Preparation of sampling plan, storage of biological data. 2. Types of biological data - quantitative and qualitative data, description, measures of location and variability, visualization, identification of outliers. 3. Random variable and probability distribution (normal, standardized normal) and their applications in biology and ecology. 4. Other types of distributions (binomial, Poisson) and their applications in biology and ecology). 5. Point and interval estimates. 6. Introduction to hypothesis testing - null and alternative hypothesis, type I. and II. errors, statistical test and its strength, p - value. 7. The problem of multiple hypothesis testing in biology and ecology and correction procedures. 8. One-sample tests - parametric and nonparametric methods. 9. Comparison of parameters of two sample populations - parametric and nonparametric methods. 10. Analysis of variance (ANOVA) - evaluation of variance of biological and ecological data, evaluation of normality, Kruskal - Wallis test - nonparametric alternative ANOVA. 11. Correlation analysis - Pearson's and Spearman's correlation coefficient, similarity measures in ecology (similarity coefficients, correlation coefficients, covariance). 12. Regression analysis - linear regression, assumptions of linear model, estimation of regression model parameters, detemination coefficient, basic statistical tests. 13. Regression analysis - polynomial regression, basic statistical tests, residue analysis. 14. Introduction to multiple linear regression - types of variable interactions, multicollinearity, the problem of missing data, applications to biological and ecological data.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Graded credit Graded credit 100  51
Mandatory attendence parzicipation: participation in exercises project - data evaluation credit test

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2022/2023 (B0724HGF011) Environmental Protection within Industry K Czech Ostrava 2 Compulsory study plan
2022/2023 (B0724HGF011) Environmental Protection within Industry P Czech Ostrava 2 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner