+ | P:2.dr. sc. Anđa Valent prof. struč. stud. P:3.dr. sc. Reni Banov v. pred. P:prof.dr.sc. Ljiljana Arambašić A:prof.dr.sc. Ljiljana Arambašić A:dr. sc. Reni Banov v. pred. A:dr. sc. Anđa Valent prof. struč. stud. | Mathematics | 30+30 (30+0+0+0) (120) | 6 | 146749 | NO |
Code WEB/ISVU
| 30217/146749
| ECTS
| 6
| Academic year
| 2024/2025
|
Name
| Mathematics
|
Status
| 1st semester - Electrical Engineering (Izvanredni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Izvanredni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Izvanredni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Redovni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Redovni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Redovni diplomski elektrotehnika) - elective course
|
Department
|
|
Teaching mode
| Lectures + exercises (auditory + laboratory + seminar + metodology + construction) work at home
| 30+30 (30+0+0+0) 120
|
Teachers
| Lectures:1. Ivica Vuković Lectures:2. dr. sc. Anđa Valent prof. struč. stud. Lectures:3. dr. sc. Reni Banov v. pred. Lectures:prof.dr.sc. Ljiljana Arambašić Auditory exercises:prof.dr.sc. Ljiljana Arambašić Auditory exercises:dr. sc. Reni Banov v. pred. Auditory exercises:dr. sc. Anđa Valent prof. struč. stud. Auditory exercises: Ivica Vuković
|
Course objectives
| Students should be qualified to use differential and integral calculus of several variables.
| Learning outcomes: | 1.ability to write analytic expression for partial derivatives and differential of real analytic function of several variables. Level:7 2.ability to write the equation of the tangent plane to the surface at the given point of the surface. Level:7 3.ability to expand a real function of two real variables into Taylor and MacLaurin series. Level:7 4.ability to calculate and classify local extrema of a real function of two real variables. Level:7 5.ability to integrate real functions of several variables. Level:7 6.ability to apply polar coordinates to integral calculus. Level:7 7.ability to understand the implementation of integrals on calculating centre of mass, static moments and centre of gravity. Level:7 8.ability to understand methods of solving differential equations. Level:7
| Methods of carrying out lectures | Ex cathedra teaching Case studies Simulations Modelling Discussion Other The lectures are being presented in the classroom with detailed solving and analysis. | Methods of carrying out auditory exercises | Laboratory exercises, computer simulations Group problem solving Computer simulations Other The problems are being solved on the blackboard with detailed explanations. | Course content lectures | 1.Functions of several variables. Graphical representation. Curves and surfaces., 2h, Learning outcomes:1 2.Continuity and limit. Partial derivatives. Schwartz theorem., 2h, Learning outcomes:1 3.Tangent plane. Differential. Gradient., 2h, Learning outcomes:1,2 4..Taylor mean value theorem. Taylor and Maclaurin series., 2h, Learning outcomes:1,3 5.Local extrema of functions of several variables., 2h, Learning outcomes:1,4 6.Conditional extrema. Lagrange multipiler method., 2h, Learning outcomes:1,4 7.Double integrals. Notion and properties., 2h, Learning outcomes:5 8.Double integrals over curvilinear trapezoid., 2h, Learning outcomes:5 9.Double integrals in polar coordinates, 2h, Learning outcomes:5,6 10.Triple integrals., 2h, Learning outcomes:5,6 11.Applications of multiple integrals. Calculatiog areas and volumes., 2h, Learning outcomes:5,6,7 12.Applications of multiple integrals. Center of mass., 2h, Learning outcomes:5,6,7 13.Homogenous systems of differential linear equations second order with constant coefficients., 2h, Learning outcomes:8 14.Nonhomogenous systems of differential linear equations with constant coefficients., 2h, Learning outcomes:8 15.Systems of linear differential equations. Numerical methods., 2h, Learning outcomes:8
| Course content auditory | 1.Functions of several variables. Domains and graphs., 2h, Learning outcomes:1 2.Partial derivatives. Schwarz theorem, 2h, Learning outcomes:1 3.Tangent plane. Differential. Gradient., 2h, Learning outcomes:1,2 4.Taylor mean value theorem. Taylor and Maclaurin series, 2h, Learning outcomes:1,3 5.Local extrema of functions of several variables., 2h, Learning outcomes:1,4 6.Conditional extrema. Lagrange multiplier method., 2h, Learning outcomes:1,4 7.Double integrals. Notion and properties., 2h, Learning outcomes:5 8.Double integrals over curvilinear trapezoid., 2h, Learning outcomes:5 9.Double integrals in polar coordinates., 2h, Learning outcomes:5,6 10.Triple integrals., 2h, Learning outcomes:5,6 11.Applications of multiple integrals. Calculatiog areas and volumes., 2h, Learning outcomes:4,5,6 12.Applications of multiple integrals. Center of mass., 2h, Learning outcomes:5,6,7 13.Homogenous systems of differential linear equations second order with constant coefficients, 2h, Learning outcomes:8 14.Method variation constants., 2h, Learning outcomes:8 15.Euler method and Runge-Kutta method., 2h, Learning outcomes:8
| Required materials | Basic: classroom, blackboard, chalk... General purpose computer laboratory Whiteboard with markers Overhead projector
| Exam literature | 1. I. Vuković, A. Valent: Zbirka riješenih primjera iz primijenjene matematike, Redak, 2015.
1. P. Javor, Matematička analiza 2, Element, Zagreb, 2000.
3. B. P. Demidovič: Zadaci i riješeni zadaci iz više matematike s primjenom na tehničke nauke, Tehnička knjiga, 1978.
| Students obligations | 50% of class attendance of the total class number. In case of less attendance, submitted seminar paper. | Knowledge evaluation during semester | Two 2 midterm exams
| Knowledge evaluation after semester | Written exam.
Oral exam optional.
| Student activities: | Aktivnost | ECTS | (Constantly tested knowledge) | 4 | (Written exam) | 2 |
| Remark | This course can be used for final thesis theme | Proposal made by | Ivica Vuković, Anđa Valent | |
+ | P:1.dr.sc Sonja Zentner Pilinsky prof.v.š. P:dr.sc. Winton Afrić prof.v. š. A: Antonio Milde mag. ing. el. A:dr.sc Sonja Zentner Pilinsky prof.v.š. L: Ante Golić L: Petar Jandrlić L: Antonio Milde mag. ing. el. L: Davor Predavec L:dr.sc Sonja Zentner Pilinsky prof.v.š. S: Antonio Milde mag. ing. el. S:dr.sc Sonja Zentner Pilinsky prof.v.š. | Mobile Communication Networks | 30+30 (6+12+12+0) (120) | 6 | 228472 | NO |
Code WEB/ISVU
| 30942/228472
| ECTS
| 6
| Academic year
| 2024/2025
|
Name
| Mobile Communication Networks
|
Status
| 1st semester - Electrical Engineering (Izvanredni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Izvanredni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Redovni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Redovni diplomski elektrotehnika) - elective course
|
Department
|
|
Teaching mode
| Lectures + exercises (auditory + laboratory + seminar + metodology + construction) work at home
| 30+30 (6+12+12+0) 120
|
Teachers
| Lectures:1. dr.sc Sonja Zentner Pilinsky prof.v.š. Lectures:dr.sc. Winton Afrić prof.v. š. Auditory exercises: Antonio Milde mag. ing. el. Auditory exercises:dr.sc Sonja Zentner Pilinsky prof.v.š. Laboratory exercises: Ante Golić Laboratory exercises: Petar Jandrlić Laboratory exercises: Antonio Milde mag. ing. el. Laboratory exercises: Davor Predavec Laboratory exercises:dr.sc Sonja Zentner Pilinsky prof.v.š. Seminar exercises: Antonio Milde mag. ing. el. Seminar exercises:dr.sc Sonja Zentner Pilinsky prof.v.š.
|
Course objectives
| students will be qualified to solve problems in installation and changing of mobile network functions as well as interconnection of different networks.
| Learning outcomes: | 1.ability to predict possible signal disorders in networks due to transmitter locations of users. Level:7 2.ability to evaluate and grade possible transmitter locations. Level:6,7 3.ability to propose new transmitter locations for transmitters with poor performance. Level:6,7 4.ability to plan a complete network on the basis of given data. Level:6,7 5.bility to select base and mobile stations with satisfactory characteristics as well as the other equipment. Level:7 6.ability to integrate various networks into the integral a whole to meet the users' requirements. Level:6,7 7.ability to find optimal solutions based on the required characteristics and price of the entire equipment. Level:6,7 8.ability to evaluate spectra efficiency of different systems, different system compatibility and possibility of future upgrade. Level:7
| Methods of carrying out lectures | Ex cathedra teaching Guest lecturer Discussion Questions and answers Other The subject matter is explained by using drawings, tables and diagrams to make the material easier to understand. The teacher tests the students continuously if they participate in the lectures. Beside the blackboard it is necessary to have a LCD projector. | Methods of carrying out auditory exercises | Group problem solving Other The problems of each theme are solved on the blackboard with the assistance of | Methods of carrying out laboratory exercises | Laboratory exercises on laboratory equipment
| Methods of carrying out seminars | Other students work on their semianrs and present them in a group + discussion about seminar theme | Course content lectures | 1.introduction, repetition of basic GSM principles, 2h, Learning outcomes:1,2,5 2.security in GSM systems, 2h, Learning outcomes:4,5,6 3.GPRS systems, 2h, Learning outcomes:4,5,6 4.EDGE systems, 2h, Learning outcomes:4,5,6 5.UMTS system architecture, network planning, 2h, Learning outcomes:5,6 6.UMTS network planning, 2h, Learning outcomes:4,5,6 7.HSDPA technology, 2h, Learning outcomes:6,7,8 8.HSUPA technologies, 2h, Learning outcomes:6,7,8 9.Mobile forensics, 2h, Learning outcomes:6,8 10.Introduction to LTE and LTE demands, 2h, Learning outcomes:1,4,5 11.WiMAX vs. LTE, 2h, Learning outcomes:6,7,8 12.LTE system in Croatia and LTE measurements - guest-lecturer from operator, 2h, Learning outcomes:6,7,8 13.OFDMA technologies, 2h, Learning outcomes:1,3,4,7 14.MIMO technologies, 2h, Learning outcomes:1,3,4,7 15.basic principles of 5G networks, 2h, Learning outcomes:1,3,4,7
| Course content auditory | 1.Transmit quality estimation in the presence of cochannel interference , 2h, Learning outcomes:1,2,3,4 2.Transmit quality estimation in the presence of cochannel interference, Methods of code generation for different purposes in mobile network , 2h, Learning outcomes:1,2,3,4 3.Methods of code generation for different purposes in mobile network , 2h, Learning outcomes:1,2,3,4 4.First semiexam, 2h, Learning outcomes:1,2,3 5.possibilities of tracing user who uses more SIM cards and more MSs, 2h, Learning outcomes:5,6 6.-, 2h 7.-, 2h 8.-, 2h 9.-, 2h 10.-, 2h 11.-, 2h 12.-, 2h 13.-, 2h 14.-, 2h 15.-, 2h
| Course content laboratory | 1.-, 2h 2.-, 2h 3.-, 2h 4.-, 2h 5.-, 2h 6.-, 2h 7.II semiexam, 2h, Learning outcomes:1,2,3,4,5,6,7 8.3G measurements and measurements analysis in computer programme Nemo Outdoor , 2h, Learning outcomes:7 9.4G measurements and measurements analysis in computer programme Nemo Outdoor , 2h, Learning outcomes:7 10.measurements conversion from Nemo Outdoor to Excall and measurements analysis, 2h, Learning outcomes:7 11.III semiexam, 2h, Learning outcomes:1,2,3,4,5,6,7 12.-, 2h 13.-, 2h 14.-, 2h 15.-, 2h
| Course content seminars | 1.-, 2h 2.-, 2h 3.-, 2h 4.-, 2h 5.-, 2h 6.seminar themes preparation , 2h, Learning outcomes:1,2,3,4,5,6,8 7.-, 2h 8.-, 2h 9.-, 2h 10.-, 2h 11.-, 2h 12.students present their seminar papers, 2h, Learning outcomes:1,2,3,4,5,6,7,8 13.students present their seminar papers, 2h, Learning outcomes:1,2,3,4,5,6,7,8 14.students present their seminar papers, 2h, Learning outcomes:1,2,3,4,5,6,7,8 15.students present their seminar papers, 2h, Learning outcomes:1,2,3,4,5,6,7,8
| Required materials | Basic: classroom, blackboard, chalk... Whiteboard with markers Overhead projector
| Exam literature | 1.E. Zentner, Antene i radiosustavi,Graphis, Zagreb, 2001.
2. Lehpamer H.: Transmission Systems Design Handbook for Wireless Networks, Artech House, Boston-London,2002.
3. W.C.Y.Lee: Mobile Communications Design Fundamentals, McGraw-Hill, 1993.
| Students obligations | minimum of 20 class attendance (lecture and exercises), submitted and presented seminar paper and performed laboratory exercises | Knowledge evaluation during semester | Redovitost pohađanja#4#10#50$Mini-test#2#30#50$Kolokvij, numerički zadaci#3#45#50$Kolokvij, teorijska pitanja#3#15#50$ | Knowledge evaluation after semester | •The written part of the exam (in case that both preliminary are not passed) and the oral part of the exam (final mark is arithmetic middle of 2 components in case that they are all positive)
| Remark | This course can be used for final thesis theme | ISVU equivalents: | 22631;146783; | Proposal made by | professor Sonja Zentner Pilinsky, Ph.D. | |
+ | P:1. Goran Vujisić P:2.dr. sc. Tomislav Špoljarić d. i. e., prof. struč. stud. L: Hrvoje Benić L: Nina Šare pred. L: Goran Vujisić K: Hrvoje Benić K: Nina Šare pred. K: Goran Vujisić | Process Modelling and Simulation | 30+30 (0+15+0+15) (120) | 6 | 146765 | NO |
Code WEB/ISVU
| 30226/146765
| ECTS
| 6
| Academic year
| 2024/2025
|
Name
| Process Modelling and Simulation
|
Status
| 1st semester - Electrical Engineering (Izvanredni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Izvanredni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Redovni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Redovni diplomski elektrotehnika) - elective course
|
Department
|
|
Teaching mode
| Lectures + exercises (auditory + laboratory + seminar + metodology + construction) work at home
| 30+30 (0+15+0+15) 120
|
Teachers
| Lectures:1. Goran Vujisić Lectures:2. dr. sc. Tomislav Špoljarić d. i. e., prof. struč. stud. Laboratory exercises: Hrvoje Benić Laboratory exercises: Nina Šare pred. Laboratory exercises: Goran Vujisić Construction exercises: Hrvoje Benić Construction exercises: Nina Šare pred. Construction exercises: Goran Vujisić
|
Course objectives
| Students will acquire knowledge of the control and regulation properties of the process elements by modeling and simulating
| Learning outcomes: | 1.ability to formulate/create a task for analysis. Level:6,7 2.ability to write a mathematical model of the process. Level:6,7 3.ability to create a Simulink model. Level:6,7 4.ability to choose a controller type . Level:7 5.ability to estimate the transient response of open-loop and closed-loop system. Level:7 6.ability to generalize conduct of electrical, mechanical, electromechanical and thermal processes and the process the fluid storage. Level:6,7
| Methods of carrying out lectures | Ex cathedra teaching Case studies Simulations Modelling Other The matter is presented by mathematical models, tables and diagrams using illustrative examples in practice. | Methods of carrying out laboratory exercises | Laboratory exercises on laboratory equipment Laboratory exercises, computer simulations Group problem solving Computer simulations Other Exercises are performed in PC laboratory by using Matlab/Simulink programs. | How construction exercises are held | Laboratory exercises, computer simulations Discussion, brainstorming Computer simulations
| Course content lectures | 1.Introduction, 3h, Learning outcomes:1 2.Modeling methods., 3h, Learning outcomes:1 3.Setting up and making a model., 3h, Learning outcomes:1,2 4.Simulation program package Matlab / Simulink., 3h, Learning outcomes:3 5.Simple and complex models., 3h, Learning outcomes:1,2,3 6.Connecting different elements into a process model., 3h, Learning outcomes:3,5 7.Differential equation simulation and usage of a model to simulate differential equations., 3h, Learning outcomes:2,3,5 8.Numerical integrations, specific usage of different toolboxes., 3h, Learning outcomes:2,3,4 9.Multistage amplifiers, 3h, Learning outcomes:1,2,3,6 10.Exemples of process models., 3h, Learning outcomes:1,2,3,4,5,6 11.No class. 12.No class. 13.No class. 14.No class. 15.No class.
| Course content laboratory | 1.No class. 2.No class. 3.No class. 4.Modelling of electrical system., 2h, Learning outcomes:1,2,3 5.Modelling of mechanical system., 2h, Learning outcomes:1,2,3 6.Modelling of electromechanical system., 2h, Learning outcomes:1,2,3,4 7.Modelling of thermal system., 2h, Learning outcomes:1,2,3,4 8.Modelling of fluid storage system. , 2h, Learning outcomes:1,2,3 9.Numerical integration procedure - properties and selection for specific use., 2h, Learning outcomes:2 10.Connection of PLC system and real-time process model in PC., 3h, Learning outcomes:1,2,3,4,5,6 11.No class. 12.No class. 13.No class. 14.No class. 15.No class.
| Course content constructures | 1.No class. 2.No class. 3.No class. 4.No class. 5.No class. 6.No class. 7.No class. 8.No class. 9.No class. 10.No class. 11.Development of a mathematical model., 5h, Learning outcomes:1,2 12.Process and system parametar identification, 5h, Learning outcomes:2,3,4 13.Modeling and verification of mathematical Simulink model., 5h, Learning outcomes:1,2,3,4,5,6 14.No class. 15.No class.
| Required materials | Basic: classroom, blackboard, chalk... General purpose computer laboratory Special purpose computer laboratory Overhead projector Special equipment Program package Matlab | Exam literature | 1. Ž. Ban,J. Matuško, I. Petrović: Primjena programskog sustava MATLAB, Graphis, Zagreb, 2010.
2. Ž. Ban: Simulacijski paketi u analizi i sintezi sustava automatskog upravljanja: FER-ZAPR
Zagreb, 1999.
3. N. Perić,I. Petrović: Automatizacija postrojenja i procesa, FER Zagreb, Zagreb, 2005.
4. D Hanselman, B. Littlefield: Mastering Matlab; Prentice Hall, New Jersey, 1996.
5. *** Simulik User`s Guide the Matlab Works Inc, 1993.
| Students obligations | Attendance at all laboratory exercises and development of construction task | Knowledge evaluation during semester | Labaratory exercise pass, all written reports.
Completed constuction task - oral exam. | Knowledge evaluation after semester | Completed constuction task
Prezentation of construction task. Oral exam.
| Student activities: | Aktivnost | ECTS | (Practical work) | 2 | (Constantly tested knowledge) | 2 | (Written exam) | 2 |
| Remark | This course can be used for final thesis theme | ISVU equivalents: | 22617;159771; | Proposal made by | Senior lecturer Mato Fruk, dipl. ing. | |
+ | P:1.doc.dr.sc. Aleksander Radovan , prof. struč. stud., dipl.ing. L:doc.dr.sc. Aleksander Radovan , prof. struč. stud., dipl.ing. | Object oriented programming | 30+30 (0+30+0+0) (60) | 4 | 201727 | NO |
Code WEB/ISVU
| 30746/201727
| ECTS
| 4
| Academic year
| 2024/2025
|
Name
| Object oriented programming
|
Status
| 1st semester - Electrical Engineering (Izvanredni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Izvanredni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Izvanredni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Redovni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Redovni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Redovni diplomski elektrotehnika) - elective course
|
Department
|
|
Teaching mode
| Lectures + exercises (auditory + laboratory + seminar + metodology + construction) work at home
| 30+30 (0+30+0+0) 60
|
Teachers
| Lectures:1. doc.dr.sc. Aleksander Radovan , prof. struč. stud., dipl.ing. Laboratory exercises:doc.dr.sc. Aleksander Radovan , prof. struč. stud., dipl.ing.
|
Course objectives
| Acquiring knowledge and skills for development of Java applications that use the database.
| Learning outcomes: | 1.write a code for a JavaFX application which will use a GUI and a database. Level:6,7 2.a Java development option if it is suitable for solving a specific task. Level:7 3. elements of an application into classes, interfaces and packages according to the principles of OOP. Level:6,7 4. JavaFX applications to solve various types of practical problems. Level:6,7 5. individually the appropriateness of using Java in solving a specific practical problem. Level:7 6.development environment (Eclipse) for an efficient development of JavaFX applications. Level:6,7 7. the structure of classes in Java applications to make it upgradable. Level:6,7 8. the possibilities of upgrading an application by means of open source libraries. Level:6,7 9.redesign the existing applications by using Java. Level:6,7 10.relate the knowledge of Java to the knowledge of other programming languages. Level:6,7 11.provide a critical review of the advantages and disadvantages of Java when compared to other programming languages. Level:7 12.choose the option to use advanced language features such as lambda expressions for solving programming tasks. Level:7
| Methods of carrying out lectures | Ex cathedra teaching Case studies Demonstration Discussion Questions and answers
| Methods of carrying out laboratory exercises | Other Practical work using computer with Java development environment installed. | Course content lectures | 1.Java programming language basics and simple Java programs, 2h, Learning outcomes:2,5,10,11 2.Classes and objects in Java, 2h, Learning outcomes:7 3.Object oriented programming in Java, 2h, Learning outcomes:3 4.Exceptions in Java, 2h, Learning outcomes:7,8,9,11 5.Collections, generics and Javadoc, 2h, Learning outcomes:1,3,4 6.Files in Java, 2h, Learning outcomes:7,9,11 7.JavaFX, 2h, Learning outcomes:1,3,4,6 8.JDBC, 2h, Learning outcomes:1,2,3,6 9.No classes, 2h 10.No classes, 2h 11.No classes, 2h 12.No classes, 2h 13.No classes, 2h 14.No classes, 2h 15.No classes, 2h
| Course content laboratory | 1.No classes, 2h 2.Classes and objects in Java, 2h, Learning outcomes:3,7,10 3.Object oriented programming in Java, 2h, Learning outcomes:2,3,4,5,7,9,10 4.Exceptions in Java, 2h, Learning outcomes:7 5.Collections and generics in Java, 2h, Learning outcomes:3,5,7,9,10,11 6.Files in Java, 2h, Learning outcomes:7,8,9,10,11 7.JavaFX, 2h, Learning outcomes:1,2,4,5,6,8,9,10,11 8.JDBC, 2h, Learning outcomes:1,2,8,9,10,11 9.No classes, 2h 10.No classes, 2h 11.No classes, 2h 12.No classes, 2h 13.No classes, 2h 14.No classes, 2h 15.No classes, 2h
| Required materials | Basic: classroom, blackboard, chalk... General purpose computer laboratory Whiteboard with markers Overhead projector Students work individually in the lab, one person per workplace. Work requires use of computers with development tools for programming. | Exam literature | The Definitive Guide to Modern Java Clients with JavaFX 17: Cross-Platform Mobile and Cloud Development 2nd edition, 2021.
More Java 17: An In-Depth Exploration of the Java Language and Its Features 3rd edition, 2021.
Effective Java, 3rd edition, 2018. | Students obligations | Completing all six laboratory excercises | Knowledge evaluation during semester | Six laboratory exams - 60 points in total
Two partial exams - 20 points each
Optional points for additional effort
Every partial exam has a correctional exam
Maximum 100 points
0-49 - not good enough
50-61 - sufficient
62-74 - good
75-86 - very good
87-100 - excellent | Knowledge evaluation after semester | Written exam is evalued with 40 points, and remaining 60 points are transferred from the achievement on laboratory
exams during the semester time. | Student activities: | Aktivnost | ECTS | (Practical work) | 3 | (Written exam) | 1 |
| Remark | This course can be used for final thesis theme | ISVU equivalents: | 85745;146752; | Proposal made by | Ph.D. Aleksander Radovan, college professor, dipl.ing., 05.06.2022. | |
+ | P:1.naslovni prof. dr. sc. Renato Filjar prof. str. st. A: Darko Špoljar | Applied Statistics | 30+30 (30+0+0+0) (90) | 5 | 146751 | NO |
Code WEB/ISVU
| 30219/146751
| ECTS
| 5
| Academic year
| 2024/2025
|
Name
| Applied Statistics
|
Status
| 1st semester - Electrical Engineering (Izvanredni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Izvanredni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Redovni diplomski elektrotehnika) - elective course1st semester - Electrical Engineering (Redovni diplomski elektrotehnika) - elective course
|
Department
|
|
Teaching mode
| Lectures + exercises (auditory + laboratory + seminar + metodology + construction) work at home
| 30+30 (30+0+0+0) 90
|
Teachers
| Lectures:1. naslovni prof. dr. sc. Renato Filjar prof. str. st. Auditory exercises: Darko Špoljar
|
Course objectives
| To empower students to conduct statistical analysis of a given data set independently. To develop the probabilistic approach to problem solution.
| Learning outcomes: | 1.Comparing the differences and limitations of the data, depending on the measuring scale and distribution as well as an understanding of the distribution parameters.. Level:6,7 2.Analyze the differences between dependent and independent samples; the ability to identify a linear connection between two continuous variables. Level:6 3.define bivariate data; define a scatterplot; define the difference between linear and nonlinear dependence; recognize the negative connection from the scatterplot; define the meaning of the Pearson correlation coefficient; identify the perfect linear dependence; recognize non-linear association of two variables. Level:6,7 4.examine the conditions for the implementation of linear regression and interpretation of regression coefficients; least square method. Level:6,7 5.analyze and understand the proportions and frequencies, and creating contingency tables. Level:6 6. formulate a multiple regression; interpretation of the coefficients in the multiple regression and the comparison of the two models in a multiple regression. Level:6,7 7.select the significant variables in the regression model; understanding coefficient R2 of the final model. Level:7 8.testing assumptions for analysis of variance. Level:7
| Methods of carrying out lectures | Ex cathedra teaching Case studies Modelling Discussion Questions and answers auditory | Methods of carrying out auditory exercises | Laboratory exercises, computer simulations Group problem solving Computer simulations Laboratorijske vježbe | Course content lectures | 1.Measuring scales and distribution of some random variables with parameters that define them. Basics transformation of variables, Z - score., 2h, Learning outcomes:1 2.Statistical inference on two samples; comparisons of mean and variance. Testing the difference between means two samples - dependent and independent samples., 4h, Learning outcomes:2 3.Measures of association between two variables - correlation. Introduction to bivariate data and association (correlation)-, 4h, Learning outcomes:3 4.Simple linear regression - Introduction and prerequisites for conducting the analysis; test of homogeneity of variance, independence of observations; influential observations. Evaluation of the regression model - standardized regression coefficient; standardized regression coefficient., 3h, Learning outcomes:4 colloquium, 1h, Learning outcomes:1,2,3,4 5.Analysis of ordinal and nominal variables. Analysis kontingencijke table - speed Association - Chi Square test as a replacement for Fisher, 3h, Learning outcomes:5 6.Multiple regression. Parameter estimation method of least squares. Significance of regression coefficient. Confidence interval regression analysis., 4h, Learning outcomes:6 7.Evaluation of the regression model. Analysis of residuals and influential observations. The selection of variables in the final model. Comparison of the two models. R2, the percentage of variability previously explained, the meaning of standardized and non-standardized regression coefficients; Venn diagram; standard, sequential and stepwise regression., 4h, Learning outcomes:7 8.Analysis of variance - ANOVA. Testing assumptions. Welch ANOVA / regular ANOVA. The one-factor analysis of variance and višefektorska. The ratio of variance F test., 4h, Learning outcomes:8 9.colloqium, 1h, Learning outcomes:5,6,7,8 10.No class 11.No class 12.No class 13.No class 14.No class 15.No class
| Course content auditory | 1.Practical examples and problems related to the content of the Lecture 1, 2h, Learning outcomes:1 2.Practical examples and problems related to the content of the Lecture 2, 4h, Learning outcomes:2 3.Practical examples and problems related to the content of the Lecture 3, 4h, Learning outcomes:3 4.Practical examples and problems related to the content of the Lecture 4, 4h, Learning outcomes:4 5.Practical examples and problems related to the content of the Lecture 5, 4h, Learning outcomes:5 6.Practical examples and problems related to the content of the Lecture 6, 4h, Learning outcomes:6 7.Practical examples and problems related to the content of the Lecture 7, 4h, Learning outcomes:7 8.Practical examples and problems related to the content of the Lecture 8, 4h, Learning outcomes:8 9.no exercise 10.no exercise 11.no exercise 12.no exercise 13.no exercise 14.no exercise 15.no exercise
| Required materials | Basic: classroom, blackboard, chalk... General purpose computer laboratory Overhead projector Special equipment R environment for statistical computing, RStudio | Exam literature | Obavezna / Mandatory
1. Petzoldt, T. (2018). Data Analysis with R: Selected Topics and Examples. Technical University of Dresden. Dresden, Njemačka. Dostupno na / Available at: https://wwwpub.zih.tu-dresden.de/~petzoldt/elements_en.pdf
2. Furrer, R i suradnici. (2020). (A Gentle) Introduction to Statistics. University of Zurich. Zurich, Švicarska. Dostupno na / Available at: http://user.math.uzh.ch/furrer/download/intro2stat/script_sta120.pdf
3. Lilja, D J. (2016). Linear Regression Using R. An Introduction to Data Modeling. University of Minesota. Dostupno na:
https://open.umn.edu/opentextbooks/textbooks/linear-regression-using-r-an-introduction-to-data-modeling
Dopunska / Alternative
3. Efron, B, Hastie, T. (2016). Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press. Cambridge, UK. Dostupno na / Available at: https://web.stanford.edu/~hastie/CASI/
4. Faraway, J J. (2002). Practical Regression and ANOVA Using R. R-project. Dostupno na / Available at: https://cran.r-project.org/doc/contrib/Faraway-PRA.pdf | Students obligations | At least: 70% attendance to lectures, and 80% attendance to exericses | Knowledge evaluation during semester | Mid-term, numerical exercises, max. 100 points
evaluation:
91-100, excellent 5
81-90, very good 4
71-80, good 3
61-70, sufficient 2 | Knowledge evaluation after semester | Numerical exercises, max. 100 points
Evaluation:
91-100, excellent 5 (A)
81-90, very good 4 (B)
71-80, good 3 (C)
61-70, sufficient 2 (D) | Student activities: | Aktivnost | ECTS | (Written exam) | 5 |
| Remark | This course can be used for final thesis theme | Proposal made by | Renato Filjar, 18th June, 2019 | |