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 of one or two samples: t-tests.
7. Introduction to 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 test, test of independence for discrete variables.
12. Contingency tables, Fisher exact test, McNemar test of symmetry.
13. Designs of epidemiological studies; odds ratio; logistic regression.
Annotation
Data analysis principles. Descriptive statistics.
Introduction to inferential statistics (statistical hypothesis, statistics, significance level, p value, confidence interval). One/two independent samples procedures.
Regression. Contingency tables.