Study programmes / B-AS Agricultural Specialization / Statistical processing of data
Course code:STZPD
Course title in language of instruction:Statistické zpracování dat
Course title in Czech:Statistické zpracování dat
Course title in English:Statistical processing of data
Mode of completion and number of credits:Exam (4 credits)
(1 ECTS credit = 28 hours of workload)
Mode of delivery/Timetabled classes:full-time, 1/2
(full-time, hours of lectures per week / hours of seminars per week)
Language of instruction:Czech
Level of course:bachelor; master continuing
Semester:SS 2018/2019 - FA
Name of lecturer:doc. Ing. Václav Adamec, Ph.D. (examiner, lecturer, supervisor)
Mgr. Tomáš Zdražil (examiner, instructor)
Aims of the course:Objective of the course is mastering fundamental statistical methods and their application in biological and agricultural experimental research and practice. Strong emphasis is placed on performing statistical analyses of biological data with statistical software Statistica 12 and MS Excel. Knowledge and practical expertise gained in this course is expected to be later applied during preparation of students' diploma theses.
Course contents:
1.Basic statistical terms, stages of statistical data processing (allowance 2/2)
a.Statistical unit, population, variables
b.Data collection, processing and analysis
c.Statistical tables and diagrams

2.Properties of univariate data (allowance 2/4)
a.Data grouping, simple and interval grouping, frequency tables, histogram, barplot
b.Measures of central tendency, measures of variability, skewness, kurtosis

3.Sampling methods, random variable, distribution of random variable (allowance 2/4)
a.The principle and types of sampling, random sampling
b.Random sampling, standard error and permissible error
c.Determining sample size

4.Point estimation and interval estimation (allowance 2/2)
a.Interval estimation, one-tailed and two-tailed
b.Confidence interval for the mean, variance and standard deviation

5.Testing statistical hypotheses (allowance 4/8)
a.Principle and steps of testing statistical hypotheses, errors in statistical testing
b.One-sample and two-sample t-test, paired t-test
c.Tests of variance homogeneity, Chi-square test, F-test, Cochran test, Bartlett test, Levene test
d.Analysis of variance and analysis of covariance, subsequent testing
e.Non-parametric tests, subsequent testing

6.Linear regression and correlation analysis (allowance 2/4)
a.Regression analysis
b.Ordinary Least Squares
c.Fits, residuals, prediction
d.Model quality of fit, R2, adjusted R2
e.Correlation analysis, pairwise correlation coefficient
f.Statistical inference in regression and correlation

Learning outcomes and competences:
Generic competences:
-ability to apply knowledge
-ability to make decisions
-basic computing skills
-skilled at utilizing and processing information

Specific competences:
-A capability to apply statistical methods when evaluating experimental results
-Student have skills to obtain and explain distribution and descriptive characteristics of univariate statistical data.
-Students can choose suitable methods for analyzing biological data from experiments.
-Students have ability to interpret numerical and graphical output of statistical analyses and apply the knowledge.amination.
-Students have skills to correctly apply tests of statistical hypotheses and are capable of interpreting their output.
-Students have working knowledge of and computing skills with statistical software.

Type of course unit:required
Year of study:Not applicable - the subject could be chosen at anytime during the course of the programme.
Work placement:There is no compulsory work placement in the course unit.
Recommended study modules:none
Assessment methods:A credit is granted on the basis of two projects assessed Pass (10p or 5p) /Fail (0p), midterm written test (score at least 50%) and active participation in labs (at most 2 missed labs). Credit is required for admission to the final exam. Passing written final exam requires at least 50% score. Course grade is made on the basis of the final exam, midterm test, projects and active participation: A <100 – 110>; B <90 – 100); C <80 – 90); D <70 – 80); E <60 – 70); F <0 – 60). The examiner may shift the grade by 1 step in both directions. The course cannot be taken during overseas internship.
Learning activities and study load (hours of study load)
Type of teaching methodDaily attendance
Direct teaching
     lecture12 h
     practice24 h
     preparation for exam20 h
     preparation for regular assessment20 h
     preparation for regular testing16 h
     elaboration and execution of projects20 h
Total112 h

Basic reading list
  • STÁVKOVÁ, J. -- DUFEK, J. Biometrika. 2nd ed. Brno: Mendelova univerzita v Brně, 2012. 178 p. ISBN 978-80-7375-634-5.
  • BUDÍKOVÁ, M. -- KRÁLOVÁ, M. -- MAROŠ, B. Průvodce základními statistickými metodami. 1st ed. Praha: Grada, 2010. 272 p. Expert. ISBN 978-80-247-3243-5.
  • NEUBAUER, J. -- SEDLAČÍK, M. -- KŘÍŽ, O. Základy statistiky: Aplikace v technických a ekonomických oborech. Praha: Grada, 2016. 280 p. ISBN 978-80-247-5786-5.
Recommended reading list
  • HEBÁK, P. -- HUSTOPECKÝ, J. -- MALÁ, I. Vícerozměrné statistické metody [2]. 1st ed. Praha: Informatorium, 2005. 239 p. ISBN 80-7333-036-92.
  • MELOUN, M. -- MILITKÝ, J. Kompendium statistického zpracování dat: metody a řešené úlohy včetně CD. 1st ed. Praha: Academia, 2002. 764 p. ISBN 80-200-1008-4.
  • Statistics for engineers and scientists. Boston: McGraw-Hill, 869 p. ISBN 0-07-121492-5.
  • ŘEZÁČ, M. -- BUDÍKOVÁ, M. Statistika I.  [online]. 2010. URL:
  • SAMUELS, M L. -- WITMER, J A. Statistics for the life sciences. 4th ed. Harlow: Pearson, 2014. 634 p. Pearson new international edition. ISBN 1-292-02397-X.