Probability Methods in Engineering I
Code  Completion  Credits  Range 

W01A011  ZK  60B 
 Lecturer:
 Tutor:
 Supervisor:
 Department of Technical Mathematics
 Synopsis:

Advanced course of probability theory, involves such notions as probability space, random variable, random vector and its probability characteristics and distributions, limit theorems. In the second part, the basic principles of statistical inference will be treated.
 Requirements:
 Syllabus of lectures:

1 Classical definition of probability, probability space
2 Random variable, probability distribution
3 Probability characteristics of random variable
4 Discrete and continuous models
5 Random vector, characteristics
6 Distribution of transformed variable
7 Law of large numbers, central limit theorems
8 Statistical inference
9 Basic sample characteristics
10 Point and interval estimation. Methods of construction
11 Testing of statistical hypothesis.
12 Nonparametric tests, goodnessoffit tests
13 ANOVA
14 Regression model, analysis of residuals
 Syllabus of tutorials:

1 Classical definition of probability, probability space
2 Random variable, probability distribution
3 Probability characteristics of random variable
4 Discrete and continuous models
5 Random vector, characteristics
6 Distribution of transformed variable
7 Law of large numbers, central limit theorems
8 Statistical inference
9 Basic sample characteristics
10 Point and interval estimation.
Methods of construction
11 Testing of statistical hypothesis.
12 Nonparametric tests, goodnessoffit tests
13 ANOVA
14 Regression model, analysis of residuals
 Study Objective:
 Study materials:

Mukhopadhyay N.: Probability and statistical inference. M. Dekker Inc., 2001.
 Note:
 Further information:
 No timetable has been prepared for this course
 The course is a part of the following study plans: