CTU FEE Moodle
Probability and Statistics
B232 - Summer 23/24
Probability and Statistics - A2M01PMS
| Credits | 8 |
| Semesters | Winter |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 4+2 |
Annotation
The course covers probability and basic statistics. First classical probability is introduced, then theory of random variables is developed including examples of the most important types of discrete and continuous distributions. Next chapters contain moment generating functions and moments of random variables, expectation and variance, conditional distributions and correlation and independence of random variables. Statistical methods for point estimates and confidence intervals are investigated.
Study targets
The aim of the course is to introduce students to basics of probability and statistics.
Course outlines
1. Events and probability.
2. Sample spaces.
3. Independent events, conditional probability, Bayes' formula.
4.Random variable, distribution functin, quantile function, moments.
5. Independence of random variables, sum of independent random variables.
6. Transformation of random variables.
7. Random vector, covariance and correlation.
8. Chebyshev's inequality and Law of large numbers.
9. Central limit theorem.
10. Random sampling and basic statistics.
11. Point estimation, method of maximum likehood and method of moments, confidence intervals.
12. Test of hypotheses.
13. Testing of goodness of fit.
2. Sample spaces.
3. Independent events, conditional probability, Bayes' formula.
4.Random variable, distribution functin, quantile function, moments.
5. Independence of random variables, sum of independent random variables.
6. Transformation of random variables.
7. Random vector, covariance and correlation.
8. Chebyshev's inequality and Law of large numbers.
9. Central limit theorem.
10. Random sampling and basic statistics.
11. Point estimation, method of maximum likehood and method of moments, confidence intervals.
12. Test of hypotheses.
13. Testing of goodness of fit.
Exercises outlines
1. Events and probability.
2. Sample spaces.
3. Independent events, conditional probability, Bayes' formula.
4.Random variable, distribution functin, quantile function, moments.
5. Independence of random variables, sum of independent random variables.
6. Transformation of random variables.
7. Random vector, covariance and correlation.
8. Chebyshev's inequality and Law of large numbers.
9. Central limit theorem.
10. Random sampling and basic statistics.
11. Point estimation, method of maximum likehood and method of moments, confidence intervals.
12. Test of hypotheses.
13. Testing of goodness of fit.
2. Sample spaces.
3. Independent events, conditional probability, Bayes' formula.
4.Random variable, distribution functin, quantile function, moments.
5. Independence of random variables, sum of independent random variables.
6. Transformation of random variables.
7. Random vector, covariance and correlation.
8. Chebyshev's inequality and Law of large numbers.
9. Central limit theorem.
10. Random sampling and basic statistics.
11. Point estimation, method of maximum likehood and method of moments, confidence intervals.
12. Test of hypotheses.
13. Testing of goodness of fit.
Literature
[1] Papoulis, A.: Probability and Statistics, Prentice-Hall, 1990.
[2] Stewart W.J.: Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling. Princeton University Press 2009.
[2] Stewart W.J.: Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling. Princeton University Press 2009.
Requirements
The requirement for receiving the credit is an active participation in the tutorials.