| Course title | Statistic methods of medical research |
|---|---|
| Course code | KSZD/SMUK2 |
| Organizational form of instruction | Lecture + Seminary |
| Level of course | Master |
| Year of study | not specified |
| Semester | Summer |
| Number of ECTS credits | 2 |
| Language of instruction | Czech |
| Status of course | Compulsory |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Lecturer(s) |
|---|
|
| Course content |
|
Lecture topics: 1st consultation: 1. Continuous probability model, law of large numbers, central limit theorem, confidence intervals. 2. Principle of hypothesis testing. Chi-square tests (goodness-of-fit test, pivot table and independence test). 3. Paired and two-choice test (parametric and non-parametric version). 4. One-factor ANOVA (parametric and non-parametric version). 5. Simple linear regression and correlation. Seminar topics: 2nd consultation: 1. Frequency tables, graphs (R-project: read.table, hist, table, pie), calculations of moments and quantiles (R-project: mean, sd, median, boxplot, ecdf). 2. Normality test, goodness-of-fit test, independence test (R-project: shapiro.test, chisq.test), paired and two-sample test (R-project: t.test, wilcox.test). 3. ANOVA in case of one factor (R-project: aov, kruskal.test), simple linear regression model (R-project: lm). Self-study: 1. Statistical terminology, types of quantities, computerized data recording. 2. Description of categorical quantity, description of quantitative quantity (moments, quantiles) - definition, interpretation. 3. Selection distribution function - examples, interpretation. 4. Random phenomenon, random variable, discrete probability model (binomial, hypergeometric, Poisson). 5. Calculations of probabilities of random events (R-project: pbinom, qbinom, phyper, ppois). 6. Calculations with the Gaussian curve (R-project: dnorm, pnorm).
|
| Learning activities and teaching methods |
| unspecified |
| Learning outcomes |
|
The aim of the subject is to repeat and expand the basic knowledge of statistical description of data 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: The graduate of the course knows statistical terminology, knows selected types of statistical tests and understands the general principles of statistical induction. Professional skills: Graduates of the subject can use statistical SW. Graduate is able to choose the right descriptive methods (characteristics, graphs) and is familiar with statistical testing methods. General qualifications: The graduate of the subject is able to independently apply statistical methods in an adequate manner and can correctly interpret statistical results. |
| Prerequisites |
|
unspecified
|
| Assessment methods and criteria |
|
unspecified
min. 80% active attendance on seminars, summarized verification of knowledge and skilld (independently working with statistical SW and interpretation of results) |
| Recommended literature |
|
| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester |
|---|