Undergraduate study programme

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Probability and Statistics P402

ECTS 5 | P 30 | A 30 | L 0 | K 0 | ISVU 74047 | Academic year: 2017./2018.

Course groups

Prikaži sve grupe na predmetu

Course lecturers

RUDEC TOMISLAV, Lecturer
HARTMANN-TOLIĆ IVANA, Associate
GALIĆ RADOSLAV, Lecturer

Course description

Fundamentals of combinatorics. Algebra of events. Probability and properties. Random variable. Distribution function of a random variable. Discrete and continuous probability distributions (hypergeometric, binominal, Poisson, normal, uniform, exponential, Chi-squared, student`s t-distribution). Numerical properties of distributions. Two-dimensional probability distributions. Moments and correlations. Statistical set with parameters. Empirical and two-dimensional distributions. Correlation and regression analysis. Samples and numerical properties of samples. Parameter estimation. Interval estimation. Statistical hypothesis testing. Examples of statistical models, statistical thinking and application of statistical programmes. Writing a seminar paper.

Knowledge and skills acquired

Introduction to statistical terminology and laws, construction of statistical models and their application in engineering, process control, quality control and other problems. Preparing students for a lifelong learning process and the applicational use of mathematical tools.

Teaching methods

Mandatory lectures and exercises.

Student requirements

Definirano Okvirima kriterija ocjenjivanja studenata FERIT-a i stavkom 1.9

Monitoring of students

Definirano Okvirima kriterija ocjenjivanja studenata FERIT-a i stavkom 1.9

Student assessment

During the semester, students can take several revision exams which replace the written exam. This ensures a continuous assessment of students’ work and knowledge.

Obligatory literature

1. Galić, R. Vjerojatnost i statistika. Osijek: ETF, 2013.

2. Montgomery, D.C. Applied Statistics and Probability for engineers. USA: Wiley, 2014.

3. R. Galić, Statistika, ETFOS, Osijek, 2004


Pretraži literaturu na:

Recommended additional literature

1. Pavlić, Statistička teorija i primjena, Tehnička knjiga, Zagreb, 2000.

2. Ž. Pauše, Uvod u matematičku statistiku, Školska knjiga, Zagreb, 1995.

3. Ž. Pauše, Vjerojatnost i stohastički procesi, Školska knjiga, Zagreb, 2004

4. G. M. Clarke, D. Cooke, A Basic Course in Statistics, Arnold, London, 1992.

5. R. Galić, Vjerojatnost , ETFOS, Osijek, 2004

ECTS credits

An ECTS credit value has been added according to calculation of time required for studying and successful course completion.

Examination methods

The final assessment consists of both the written and oral exam upon completion of lectures and exercises. During the semester, students can take several revision exams replacing the written exam.

Course assessment

Provođenje sveučilišnih anketa o nastavnicima (pristup prema studentima, transparentnost kriterija, motivacija na
izvršavanje aktivnosti, jasnoća izlaganja, i sl.). Provođenje fakultetskih anketa o predmetima (nakon položenog predmeta
samoevaluacija studenata o usvojenim ishodima učenja, te o opterećenosti u usporedbi s ECTS-ima aktivnosti i predmeta
u cjelini).

Overview of course assesment

Learning outcomes
Upon successful completion of the course, students will be able to:

1. design a problem model using basic counting rules and basic concepts from combinatorics

2. construct a model for calculating a probability problem by using the rules for calculating the probability of a union and intersection of an event, as well as conditional probability rule using total probability rule and Bayes' theorem

3. design an expression to calculate a probability problem using the terms from the random variables theory

4. in the analysis of the set statistical data group, create mathematical expressions using the basic statistics formulas

5. define and distinguish the basic concepts of statistical tests and apply the appropriate statistical tests on practical examples



Learning outcomes available only as desktop version    Export to Excel
Student's activity Workload ECTS (Workload/30) Learning outcomes
Upon successful completion of the course, students will be able to:
Teaching
method
Assessment method Points
Attendance
Lectures, Auditory exercises

50
ECTS
1.7
- construct a model for calculating a probability problem by using the rules for calculating the probability of a union and intersection of an event, as well as conditional probability rule using total probability rule and Bayes' theorem- design an expression to calculate a probability problem using the terms from the random variables theory- in the analysis of the set statistical data group, create mathematical expressions using the basic statistics formulas - define and distinguish the basic concepts of statistical tests and apply the appropriate statistical tests on practical examplesLectures, Auditory exercises Attendance register. Mandatory attendance percentage is:
70%

This percentage defines the minimum workload for the activity. The maximum is defined by the study programme.
Min

0
Max

0
Practice – problem solving Workload
40
ECTS

1.3
- design a problem model using basic counting rules and basic concepts from combinatorics- design an expression to calculate a probability problem using the terms from the random variables theory- in the analysis of the set statistical data group, create mathematical expressions using the basic statistics formulas - define and distinguish the basic concepts of statistical tests and apply the appropriate statistical tests on practical examplesMidterm exam Evaluation of (written) exercises Min

20
Max

40
Oral exam Workload
45
ECTS

1.5
- design a problem model using basic counting rules and basic concepts from combinatorics- construct a model for calculating a probability problem by using the rules for calculating the probability of a union and intersection of an event, as well as conditional probability rule using total probability rule and Bayes' theorem- in the analysis of the set statistical data group, create mathematical expressions using the basic statistics formulas Oral exam Assessment of student's answers Min

25
Max

50
Homework Workload
15
ECTS

0.5
- design a problem model using basic counting rules and basic concepts from combinatorics- construct a model for calculating a probability problem by using the rules for calculating the probability of a union and intersection of an event, as well as conditional probability rule using total probability rule and Bayes' theorem- design an expression to calculate a probability problem using the terms from the random variables theory- define and distinguish the basic concepts of statistical tests and apply the appropriate statistical tests on practical examplesHomework discussion upon presentation Min

0
Max

10
Σ Activities Σ Workload
150
Σ ECTS
5
Σ Max
100