|
Lecturer(s)
|
|
|
|
Course content
|
Lecture topics: 1. Statistical terminology. Types of quantities. Description of categorical quantity. 2. Description of the numeric quantity (moments, quantiles). Selective distribution function. 3. Random phenomenon, random quantity. Probabilistic discrete variable model. 4. Probability model of a continuous quantity. Law of large numbers, central limit theorem. 5. Confidence intervals. Principle of hypothesis testing. 6. Chi-square tests (goodness of fit test, contingency table and independence test). 7. Paired and two-choice test (parametric and non-parametric version). 8. One-factor ANOVA (parametric and non-parametric version). 9. Simple linear regression and correlation. Seminar topics: 1. Computer recording of data. Frequency tables, graphs. R-project: read.table, hist, table, pie. 2. Calculations of moments and quantiles. R-project: mean, sd, median, boxplot, ecdf. 3. Calculations of probabilities of random events. R-project: pbinom, qbinom, phyper, ppois. 4. Calculations with a Gaussian curve. R-project: pexp, dnorm, pnorm. 5. Construction of confidence intervals. R-project: qnorm, qt, shapiro.test. 6. Goodness of fit test, independence test. R-project: chisq.test. 7. Carrying out a paired and two-choice test. R-project: t.test, wilcox.test. 8. Carrying out the ANOVA test in the case of one factor. R-project: aov, kruskal.test. 9. Finding and evaluating a simple linear regression model. R-project: lm.
|
|
Learning activities and teaching methods
|
|
unspecified
|
|
Learning outcomes
|
The aim of the subject is to repeat and expand basic statistical knowledge and to become familiar with the principles of statistical analysis, with an emphasis on biostatistical applications. During seminars in the computer room, students will learn to process statistical data using specialized SW (R-project).
Expertise: Student knows statistical terminology, knows selected types of statistical tests and understands the general principles of statistical induction. Professional skills: Student of the subject can use statistical software. Student is able to choose the right descriptive methods (characteristics, graphs) and is familiar with statistical testing methods. General qualifications: Student is able to independently apply statistical methods in an adequate manner and can correctly interpret statistical results.
|
|
Prerequisites
|
unspecified
|
|
Assessment methods and criteria
|
unspecified
Active participation on at least 80 % seminars, summary verification of knowledge and skills (independent work with statistical SW and interpretation of the results).
|
|
Recommended literature
|
-
HRACH, K. Sbírka úloh ze statistiky. Ústí nad Labem: UJEP, 2010. ISBN 978-80-7044-845-8..
-
HRACH, K. Základy biostatistiky s využitím Excelu. Ústí nad Labem: UJEP, 2011. ISBN 978-80-7414-398-4..
-
PEACOCK, J., PEACOCK, P. J. Oxford handbook of medical statistics. Oxford: Oxford university press, 2011. ISBN 978-0-19-955128-6..
-
ZVÁRA, K. Základy statistiky v prostředí R. Praha: Karolinum, 2013. ISBN 978-80-246-2245-3..
|