1. komponenta

Lecture type	Total
Lectures	30
Exercises	30

Upon successful completion of the course, the student is able to:
use and interpret main descriptive statistics
recognize and interpret basic notions of probability theory
apply and interpret basic statistical methods
implement basic statistical procedure in R programming environment
present results of statistical analysis

COURSE CONTENT
1. Introduction (1 hour lecture): Examples of statistical problems and data. Population and Sample. Statistical software (R).
2. Descriptive Statistics (6 hours lectures + 6 hours tutorials): Statistical variables. Representation of data in tables and graphs. Measures of central tendency and dispersion.
3. Elementary Probability (7 hours lectures + 8 hours tutorials): Probability space. Conditional probabilities. Independence. Discrete and continuous random variables. Density. Mean and expectation. Random vectors. Covariance and correlation coefficients. Binomial, Poisson and normal distributions.
4. Parameter estimation (6 hours lectures + 4 hours tutorials): Random sample. Estimation of population proportion and population mean. Confidence intervals.
5. Testing statistical hypotheses (7 hours lectures + 8 hours tutorials): The elements of a test. Errors of I and II type. Power of the test. Test of hypothesis about a population mean and proportion. Comparing two population means (t-test). Comparing two population variances (F-test). Chi squared-test.
6. Linear models (3 hours lectures + 4 hours tutorials): Linear regression model. Confidence intervals and statistical tests about the model parameters. Prediction. One-way ANOVA.

1. G. K. Bhattacharyya, R. A. Johnson, Statistical Concepts and Methods, Wiley, New York, 1977.
2. J. Pitman, Probability, Springer-Vetrlag, New York, 1993.

Enrollment :
Passed : Mathematics

Mandatory course - Regular study - Biology