Short Course on Biostatistics - Part 1, Dragieva
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CONTENTS
PROBABILITY THEORY
1. Probability: definitions, properties
1.1 Experiment, sample space
1Л Events, relations among events
1.3 Probability:' definition, properties
1.4 Counting
2. Conditional probability, total probability theorem, Bayes9 rule
2.1 Conditional probability
2.2 Total probability theorem
2.3 Bayes9 rule
3. Random variables
3.1 Definitions
3.2 Discrete random variables: distribution, characteristics ’
3.3 Some discrete random variables
3.4 Continuous random variables: definitions, properties, characteristics
3.5. Normal distribution
STATISTICS
4. Exploring the data
4.1 Data types
4.2 Organizing and graphing the data
4.3 Describing the data with numbers
4.4 Examples
5. Parameter estimation
5.1 Definitions
5.2 Estimation and confidence intervals for the population mean fix
5.3 Estimation and confidence intervals for the population proportion p
5.4 Estimation and confidence intervals for the population variance ax
6. Hypotheses testing
6.1 Definitions
6.2 One sample hypothesis tests for the population mean jux
6.3 One sample hypothesis tests for the population proportion p
6.4 One sample hypothesis tests for the population variance a\
7. Tables
