Introduction to Statistics

Course unit code

Specialization:

02.03.02 Computer science and information technologies, 02.03.03 Mathematical support and information systems administration, 09.03.03 Applied computer science, 09.03.04 Software Engineering

Course unit title

Introduction to Statistics

Name(s), surname(s) and title of lecturer(s)

Anna V. Kitaeva, professor

Level of course

Bachelor

Semester

4 or 5

ECTS credits

2

Working hours

Contact hours

48

lectures

26

seminars

practical and laboratory classes

18

consultations

4

Independent work

38

Total

86

Work placement

none

Language of instruction

English

Prerequisites

Calculus, Linear algebra, Theory probability

Objectives of the course

Learning outcomes

A student’s assessments methods

The key aim has been to develop the ability to construct and to use common statistical methods in a manner that combines intuitive understanding and mathematical precision.

While mastering the discipline the following expertise is evolved in students:

а) general: to analyze and generalize the information; to express the thoughts clearly.

b) professional: to use statistic methods in their professional activity; to choose methods for solving management and design tasks in the sphere of computer science and technology; to justify the decisions, to prove their correctness.

As a result a student should:

Know: the basic concepts and models of classic Statistics: sample space; inference, estimation; the basic methods of statistical inference: point and interval estimation, hypotheses testing, regression models.

Be able to: apply statistic methods to solve various theoretical and practical tasks; formulate and test basic statistical hypotheses; construct basic point and interval estimators of distribution’s parameters and investigate their properties, apply simple regression models to investigate the linear dependence.

Have skills of: testing classic statistical hypotheses; parameter‘s estimating by the ML and MM methods, regression investigation.

work with the course book, research and review of literature and other electronic sources on a given problem individually, homework, home tests, advanced self-study, self-study of a particular subject, exersises, tests and exam.

Teaching methods

Lectures, solving exercises, independent study of literature and other electronic sources, case studies, testing.

Course unit content

Title

Lectures  (hours)

Self-study (hours)

Practice and lab

(hours)

1. Histogram, sample distribution function, box-and-whisker plot and other methods of data representation

2

6

2

2. Point estimation

6

8

4

3. Interval estimation

6

8

4

4. Hypotheses testing

6

8

4

5. Regression models

6

8

4

26

38

18

Assessment requirements

Student’s skills in this subject will be evaluated by means of discussions at the seminars, presentation pre-determined topics, solving tasks, doing individual laboratory works, and final examination.

Assessment criteria

The assessment is carried out by the following criteria: clarity of explanation; logical thinking; achiving the specified learning standards (some percentages of the tests' performance, solved exercises), succesfull data processing.

The composition of final accumulative mark

Final accumulative mark consists of: 3 lab assignments –15% each, exam –  55%

Course outline arranged by