1. An algebra of events.
Events, outcomes, the complement of an outcome. Operations over events.
2. Introduction to probability.
The addition law. Mutually exclusive events, conditional probability, independent events, the multiplication law. Bayes´ theorem.
3. Discrete random variables. Summary of discrete probability distributions.
4. Continuous random variables. Summary of continuous probability distributions.
5. Special cases of continuous distributions. Limit theorems.
6. Data, measurment. Summarizing data, graphs.
7. Numerical descriptive statistics. Measures of location.
8. Measures of dispersion.
9. Characteristics of shape of data sets.
10. Bivariate data, correlation coefficient.
11. Simple linear regression.
12. Populations, samples, statistical inference - preview.
13. Estimations. Point estimators, interval estimation of population characteristics.
14. Hypothesis testing - principles.
Events, outcomes, the complement of an outcome. Operations over events.
2. Introduction to probability.
The addition law. Mutually exclusive events, conditional probability, independent events, the multiplication law. Bayes´ theorem.
3. Discrete random variables. Summary of discrete probability distributions.
4. Continuous random variables. Summary of continuous probability distributions.
5. Special cases of continuous distributions. Limit theorems.
6. Data, measurment. Summarizing data, graphs.
7. Numerical descriptive statistics. Measures of location.
8. Measures of dispersion.
9. Characteristics of shape of data sets.
10. Bivariate data, correlation coefficient.
11. Simple linear regression.
12. Populations, samples, statistical inference - preview.
13. Estimations. Point estimators, interval estimation of population characteristics.
14. Hypothesis testing - principles.