Syllabus
1) Introduction.
2) Descriptive statistics.
3) Basics of probability theory (random events, the definition of probability, conditional probability, independent events).
4) Random variable and its distribution. Characteristics of random variable. Examples of probability distributions.
5) Random vectors. Independent random variables, correlation.
6) Random sample. The law of large numbers. The central limit theorem.
7) Probabilistic and statistical approach in exploring real world. Estimates of the random variable characteristics.
8) Estimation theory. Hypothesis testing. Mathematical statistics as a basic tool for drawing conclusions from a scientific experimental work.
9) Selected statistical tests (one sample test, two sample test, paired test, some nonparametric tests, independence testing in contingency table).
10) Linear regression model.
Annotation
Basic concepts of probability theory and mathematical statistics: random event, probability, conditional probability, independence, correlation, random variable and its distribution, descriptive statistics, estimation of random variable characteristics, selected statistical tests. The lecture is oriented to understanding the subject with respect to applications in chemistry.