Course detail

# Mathematics 3

FEKT-BKC-MA3Acad. year: 2022/2023

The aim of this course is to introduce the basics of two mathematical disciplines: numerical methods, and probability and statistics.

In the field of probability, main attention is paid to random variables, both discrete and continuous. The end of the course of probability is devoted to hypothesis testing.

In the field of numerical mathematics, the following topics are covered: root finding, systems of linear equations, curve fitting (interpolation and splines, least squares method), numerical differentiation and integration.

Language of instruction

Number of ECTS credits

Mode of study

Guarantor

Department

Learning outcomes of the course unit

In the field of probability and statistics:

- compute the basic characteristics of statistical data (mean, median, modus, variance, standard deviation)

- choose the correct probability model (classical, discrete, geometrical probability) for a given problem and compute the probability of a given event

- compute the conditional probability of a random event A given an event B

- recognize and use the independence of random events when computing probabilities

- apply the total probability rule and the Bayes' theorem

- work with the cumulative distribution function, the probability mass function of a discrete random variable and the probability density function of a continuous random variable

- construct the probability mass functions (in simple cases)

- choose the appropriate type of probability distribution in model cases (binomial, hypergeometric, exponential, etc.) and work with this distribution

- compute mean, variance and standard deviation of a random variable and explain the meaning of these characteristics

- perform computations with a normally distributed random variable X: find probability that X is in a given range or find the quantile/s for a given probability

- construct estimates of uknown parameters of the known distribution

- estimate parameters of a probability distribution by means of the maximum likelihood method.

In the field of numerical methods, the student should be able to:

- find the root of a given equation f(x)=0 using the bisection method, Newton method or the iterative method, describe these methods including the convergence conditions

- find the root of a system of two equations using Newton or iterative method

- solve a system of linear equations using Gaussian elimination with pivoting, Jacobi and Gauss-Seidel iteration methods, discuss the advantages and disadvantages of these methods

- find Lagrange or Newton interpolation polynomial for given points and use it for approximating the given function

- find the approximation of a function by spline functions

- find the approximation of a function given by table of points by the least squares method (linear, quadratic or exponential approximation)

- choose the most convenient type of approximation (interpolation polynomial, spline, least squares)

- estimate the derivative of a given function using numerical differentiation

- compute the numerical approximation of a definite integral using trapezoidal and Simpson method

- in all the above cases discuss principles of the respective methods, , choose a suitable method for a given task, discuss their convergence and justify one's reasoning

Prerequisites

From the BMA1 and BMA2 courses, the basic knowledge of differential and integral calculus is demanded. Especially, the student should be able to sketch the graphs of elementary functions, to substitute into functions, to compute derivatives (including partial derivatives) and integrals.

Co-requisites

Planned learning activities and teaching methods

Assesment methods and criteria linked to learning outcomes

Course curriculum

2. Numerical methods of solution of one non-linear equation and of non-linear systems.

3. Interpolation polynomials and splines.

4. Least squares method. Numerical differentiation and integration.

5. Introduction to probability theory.

6. Random variables and their numerical characteristics.

7. Random vectors and their numerical characteristics.

8. Selected probability distributions.

9. Law of large numbers, central limit theorem.

10. Introduction to statistics. Statistical processing of data.

11. Point and interval estimates. Method of moments and maximum likehood estimation.

12. Statistical tests with normal distribution, Pearson's chi-squared test.

13. Non-parametric tests.

Work placements

Aims

Specification of controlled education, way of implementation and compensation for absences

Recommended optional programme components

Prerequisites and corequisites

Basic literature

Recommended reading

eLearning

**eLearning:**currently opened course

#### Type of course unit

Lecture

Teacher / Lecturer

Syllabus

2. Random variables, random vector, distribution function.

3. Characteristics of random variables, basic distributions.

4. Characteristics of random vectors, covariance, correlation.

5. Law of large numbers, Central limit theorem.

6. Introduction to statistics, histogram,

7. Moment method, maximum likelihood method.

8. Numerical solution of systems of nonlinear equations. Systems of linear equations (Gaussian elimination with pivoting, Jacobi and Gauss-Seidel iterative methods).

9. Interpolation: interpolation polynomial (Lagrange and Newton), splines (linear and cubic)

10. Least squares approximation. Numerical differentiation.

11. Numerical integration (trapezoidal and Simpson method).

12. Numerical solution of differential equations: initial problems (Euler method and its modifications, Runge-Kutta methods), boundary value problems (very briefly).

Computer-assisted exercise

Teacher / Lecturer

Syllabus

2. Conditional probability, total probability rule and Bayes theorem

3. Discrete random variables, discrete distributions

4. Continuous random variables

5. Normal distribution, normal approximation to binomial distribution

6. Hypothesis testing

7. Root separation, bisection, Newton and iterative methods

8. Interpolation polynomial, spline functions

9. Least squares method

10. Numerical differentiation and integration

11. Numerical solution of differential equations - Euler and Runge-Kutta methods

eLearning

**eLearning:**currently opened course