Lectures: 1.Introduction to biostatistics. Random variable. Population and sample. Characteristic of variables, frequency distribution, probability distribution, quantiles. Descriptive statistics - measures of central tendency and measures of dispersion and variability. 2. Probability distributions: Gaussian normal, non-normal distr., Student's t-distribution, Pearson's Chi-Square distr., Fisher's F-distribution. Estimation of population parameters, confidence intervals for the mean value, standard deviation and median. 3. Testing of statistical hypotheses. Parametric tests. F-test, Student's t-test. Analysis of variance (ANOVA). Multiple comparisons tests. 4. Nonparametric tests for hypotheses on continuous variables with nonnormal distribution. Mann-Whitney test, Wilcoxon test, sign test. 5. Relations between two variables. Regression analysis - simple linear regression. Correlation analysis. Significance of the correlation coefficient. Non-linear regression. 6. Probabilities and biostatistics, binomial distribution. Categorical data, estimation of frequencies. Test for difference between empirical and theoretical frequency, testing for difference between 2 empirical frequencies. 7. Relationship among categorical data. Testing for relationships among categorical data-contingency tables 2x2, contingency tables k x m. Practices: 1.Introduction practice. 2.MS Excel - basic calculations, use of biostatistical formulas, graphical presentation of data. 3.Descriptive statistics - calculations by means of calculator and MS Excel. 4.MS Excel. Descriptive characteristics, F-test. Examples 5.Student´s t-test: 1-samle, 2-sample: paired, nonpaired. F-test. Examples. 6.Statistical sw UNISTAT - basic statistical parameters of data files, parametric tests: F-test, t-test. 7.UNISTAT - solving of model examples (F-test, t-test). 8.MS Excel - statistical functions (F-test, t-test), graphical presentation of data. 9.Statistical sw UNISTAT - Statistical data files processing: ANOVA. Model examples. 10.UNISTAT, MS Excel - Statistical data files processing: correlation and regression analysis. Model examples. 11.MS Excel - individual examples - evaluation of experimental data. 12.MS Excel, UNISTAT - individual work - statistical evaluation of data. 13.Credit.
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