Probability and Statistics

Data is displayed for academic year: 2023./2024.

Associate Lecturers

Laboratory exercises

Course Description

probability, conditional probability, discrete random variables and random vectors, continuous random variables and random vectors, limit theorems, descriptive statistics, point and interval estimations, statistical tests.

Study Programmes

University undergraduate
[FER3-EN] Computing - study
(3. semester)
[FER3-EN] Electrical Engineering and Information Technology - study
(4. semester)

Learning Outcomes

  1. solve problems of evaluating probability of a given event
  2. recognize specific discrete or continuous distribution
  3. solve problems of evaluating expectation and variance of some distribution
  4. analyze given data
  5. solve problems of point and interval estimation
  6. use statistical tests
  7. demonstrate ability for mathematical modelling
  8. use critical thinking

Forms of Teaching


4 hours per week


1 hour per week

Independent assignments

each student must solve some problems on their own


6 hours per semester, 2 out of those 6 hours students work on their own

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Quizzes 0 % 10 % 0 % 10 %
Mid Term Exam: Written 0 % 50 % 0 %
Final Exam: Written 0 % 40 %
Exam: Written 0 % 90 %

Week by Week Schedule

  1. Probability; equally likely outcomes; geometric probability
  2. Conditional probability; independence; law of yotal probability; Bayes' rule
  3. Discrete random variables and random vectors; marginal distribution; conditional distribution
  4. Moments; characteristic function; generating functions
  5. Geometric Distribution; Binomial Distribution; Poisson Distribution
  6. Random variables; probability distributions; probablitiy densities, Functions of random variables
  7. Exponential distribution; normal distribution
  8. Midterm exam
  9. Random vectors; conditional probability distributions
  10. Functions of random vectors, Law of large numbers and central limit theorem
  11. Measures of central tendency (mean, median, mode); measures of dispersion (standard deviation, variance, quantile, and IQR), Unbiased point estimations; Maximal-likelihood estimation
  12. Interval estimations; confidence intervals, Confidence Intervals for parameters of normal distribution
  13. Hypothesis testing; type of errors; parametric hypothesis testing; sampling distributions
  14. Hypothesis testing; type of errors; parametric hypothesis testing; sampling distributions, Pearson's Chi-squared Test (Goodness-of-fit tests, tests of independence and homogeneity)
  15. Final exam


(2018.), N.Elezović: Vjerojatnost i statistika, Element, Zagreb
(1989.), Ž. Pauše, Uvod u matematičku statistiku, Školska knjiga, Zagreb
(1989.), Ž. Pauše, Riješeni primjeri zadaci iz vjerojatnosti i statistike, Školska knjiga, Zagreb

For students


ID 209650
  Winter semester
L0 English Level
L2 e-Learning
60 Lectures
0 Seminar
15 Exercises
6 Laboratory exercises
0 Project laboratory

Grading System

85 Excellent
70 Very Good
55 Good
45 Sufficient