Syllabus
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.
Annotation
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.