# Applied Statistics

This web page lists courses with the full set of in-class English lectures. Additional courses that are also available to English-speaking students, are accessible by clicking above links Winter Semester and Summer semester.

Higher education teachers: Dolinar Gregor
Collaborators: Hajdinjak Melita
Subject code: 64257

## Subject description

Prerequisits:

• Enrolment into the study programme.

Content (Syllabus outline):

Basic concepts of probability: Combinatorics (permutations, combinations, …). Random variables (discrete, continuous) and their distributions (Gauss, Poisson, Weibull, …). Numerical characteristics (expected value, variance)

Statistics:

Statistic design: definition of statistical hypothesis, sampling plans. Data presentation. Estimation of parameters (definition and properties of estimators). Hypothesis testing (type one and type two error), confidence intervals, tests: parametric, non-parametric. Regression and correlation (linear, bivariate, multivariate). Time series (ARIMA, ARCH). Simulations (Monte Carlo method).

Objectives and competences:

Grasp the basics of probability theory and statistical methods. Being able to collect and interpret statistical data and to make a critical analysis of the results and measurements in technical engineering with appropriately chosen statistical methods. Use of some statistical data analysis software.

Intended learning outcomes:

Become familiar with statistical methods used in technical engineering, being able to distinguish among them, being able to make a statistical analysis with the help of appropriatestatistical software packages.

Learning and teaching methods:

• lectures,
• laboratory work,
• homeworks,
• seminar assignment.

## Study materials

1. D. C. Montgomery, G. C. Runger: Applied statistics and probability for engineers, John Wiley & Sons, 6th Edition, 2013
2. W. C. Navidi: Statistics for Engineers and Scientists, McGraw-Hill, 2007
3. G. Turk: Verjetnostni račun in statistika, Ljubljana, 2011
4. M. Hladnik: Verjetnost in statistika, Založba FE in FRI, Ljubljana, 2002
5. R.S. Kenett, S. Zacks, D. Amberti: Modern Industrial Statistics: with Applications in R, MINITAB, and JMP, Wiley 2014

# Study in which the course is carried out

• 1 year - 2nd cycle - Electrical Engineering - Information and Communication Technologies
• 1 year - 2nd cycle - Electrical Engineering - Electronics
• 1 year - 2nd cycle - Electrical Engineering - Electrical Power Engineering
• 1 year - 2nd cycle - Electrical Engineering - Biomedical Engineering
• 1 year - 2nd cycle - Electrical Engineering - Control Systems and Computer Engineering
• 1 year - 2nd cycle - Electrical Engineering - Mechatronics
• 1 year - 2nd cycle - Electrical Engineering - Robotics