Last update: RNDr. Monika Pecková, Ph.D. (14.03.2019)
This is an introductory course of biostatistics. The students will learn the principles of estimation and statistical testing. Statistical methods will be applied on the types of data commonly encountered in medicine and biology. The students will learn to analyse the data independently, using statistical package R.
Last update: RNDr. Monika Pecková, Ph.D. (14.03.2019)
Předmět je úvodem do biostatistiky. Studenti se naučí základy pravděpodobnosti a principy statistického uvažování, zejména odhadování a testování statistických hypotéz. Statistické metody budou aplikovány na typy dat běžně se vyskytující v medicíně a biologii. Důraz bude kladen nejenom na schopnost správně používat statistické metody, ale především na schopnost správně interpretovat výsledky. Cvičení budou probíhat v počítačové laboratoři, kde se studenti se naučí samostatně analyzovat data s použitím statistického programu R.
Literature -
Last update: RNDr. Monika Pecková, Ph.D. (14.03.2019)
Robert F. Woolson, William R. Clarke: Statistical Methods for the Analysis of Biomedical Data.
Noel S. Weiss, Thomas D. Koepsell: Epidemiologic Methods
Last update: RNDr. Monika Pecková, Ph.D. (14.03.2019)
Robert F. Woolson, William R. Clarke: Statistical Methods for the Analysis of Biomedical Data
Noel S. Weiss, Thomas D. Koepsel: Epidemiologic Methods
Requirements to the exam -
Last update: RNDr. Jana Rubešová, Ph.D. (05.03.2019)
Several homeworks will be reqiuired. The course exam will be practical (in class analysis of data and discusssion).
Last update: RNDr. Jana Rubešová, Ph.D. (05.03.2019)
Zápočet: několik domácích úkolů.
Zkouška bude praktická, půjde o analýzu dat a diskusi o výsledku.
Syllabus -
Last update: RNDr. Jana Rubešová, Ph.D. (05.03.2019)
1. Types of data; samples and populations; descriptive statistics. 2. Introduction to probability; independence; Bayes theorem. 3. Random variables; probability distributions; quantiles; mean; variance.
Student's t, chi-square; central limit theorem. 5. Introduction to estimation and hypothesis testing; confidence intervals. 6. Testing the hypotheses about the mean of one sample, one sample t-test; confidence interval for the mean.
7. Testing the hypotheses about the means of two samples; paired t-test; two-sample t-test; nonparametric tests. 8. Introduction to analysis of variance. 9. Correlation; simple regression; least squares method; assumptions of regression.
10. Multiple regression models, confounding, choice of the model. 11. Multinomial distribution, goodness-of-fit tests, tests of independence for discrete variables.
12. Contingency tables, Fisher exact test, McNemar test of symmetry.
13. Designs of epidemiological studies; disease frequency; estimation of risk in epidemiology.
Last update: RNDr. Jana Rubešová, Ph.D. (05.03.2019)
1. Types of data; samples and populations; descriptive statistics. 2. Introduction to probability; independence; Bayes theorem. 3. Random variables; probability distributions; quantiles; mean; variance.
4. Discrete distributions: binomial, Poisson; continuous distributions: normal, Student's t, chi-square; central limit theorem. 5. Introduction to estimation and hypothesis testing; confidence intervals. 6. Testing the hypotheses about the mean of one sample, one sample t-test; confidence interval for the mean.
7. Testing the hypotheses about the means of two samples; paired t-test; two-sample t-test; nonparametric tests. 8. Introduction to analysis of variance. 9. Correlation; simple regression; least squares method; assumptions of regression.
10. Multiple regression models, confounding, choice of the model. 11. Multinomial distribution, goodness-of-fit tests, tests of independence for discrete variables.
12. Contingency tables, Fisher exact test, McNemar test of symmetry.
13. Designs of epidemiological studies; disease frequency; estimation of risk in epidemiology.
