Probability And Statistics
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Course informations
| Study program level |
Undergraduate |
| Study program |
Electrical Engineering |
| Study program direction |
Telecommunications and informatics |
| Course year |
2. |
| Course semester |
IV |
| Course status |
Elective |
| ECTS |
6 |
| Lectures (h) |
30 |
| Excercises (h) |
30 |
| Seminars (h) |
- |
Course objectives
Acquiring knowledge and skills for independent work and at the same time preparing the students for a successful continuation of education. Introduce students with chapters of statistics needed to solve engineering problems. Analyzing real-life problems and creating the appropriate statistical models and critical reviews of the obtained results.
Course outcomes
- Combine elementary combinatorial techniques in calculating discrete probabilities. • Calculate probabilities of elemental events and events in discrete probabilistic space.
- Select a sample needed to obtain relevant data, make (graph, chart, map) different types of graphical statistical data (histogram, polygon frequency), edit an unbroken series of empirical statistics, and group and tabulate them, calculate the basic numerical characteristics of the statistical sequence (arithmetic mean, mode, quartile, variance, standard deviation).
- Distinguish basic discrete and continuous (continuous) distributions.
- Select the method to be used when processing data, perform simpler statistical surveys, explain statistical methods, describe the data obtained, and analyze them.
Course content
Binomial lesson. Random case, elemental events, events, classical definitions of probability (a priori and a posteriori), classical (discrete) probability spaces, geometric probability, conditional probability, independence of events, Bernoulli's scheme, formula of complete probability, Bayesian formula. Concept and task statistics.
Statistical features: concept, types and characteristics. Observation and data collection. Defining data. Graphical and table representation of data. Absolute and relative frequencies. Cumulative frequency function. Polygon frequency. Histogram of mean value: arithmetic mean, geometric mean, harmonic medium, mod, median, quartile. Dispersion: variation range, variance, standard deviation. Measures of asymmetry and roundness. Discrete random variables and their basic numerical characteristics.
Examples of discrete distributions: uniform distribution, binomial distribution, Poisson distribution, continuous random variables, normal distribution, sample method.
Correlation: linear correlation, rank correlation. Regression:single linear regression, coefficient rating, deviation. Analysis of time series. Graphic representation. Individual and aggregate indices. Average values. Linear trend.
