# Probability & Statistics

## Description

**COURSE OBJECTIVES**

At the end of the course, the student will be able to: - define a probability space - understand random variables and probability distributions - calculate moments of random variables - understand Chebychev's inequality, law of large numbers and central limit theorem. - apply techniques to find point estimators - evaluate goodness of estimators - understand and apply hypothesis tests - apply linear regression

**COURSE DESCRIPTION**

Probability theory is a deductive science describing the axioms for calculating the probability of some event given some known state of the world. In the first part of the course, we define the probability axioms, introduce the concept of events, random variables, and probability distributions. In inductive practice we are interested to learn about the state of the world given some event, i.e., the data. Students will learn about estimation procedures and linear regression.

**LEARNING METHODS**

The course employs lectures to explain the material and tutorials to familiarize students with the concepts introduced in class.

**EXAMINATION INFORMATION**

The course consist of 12 homework assignments that each count for 2% of the final grade. A final exam counts for 76% of the final grade.

**REFERENCES**

- Probability and mathematical statistics, Sahoo, 2013.

## People

## Additional information

**Fall**

**2020-2021**

**6**

**English**

**Bachelor of Science in Informatics, Core course, Lecture, 2nd year**

**Master of Science in Management and Informatics, Management track, Lecture, 1st year**