Applied Statistics

Subject description

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).

The subject is taught in programs

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.

Teaching and learning methods

Lectures, laboratory work, homeworks, seminar assignment.

Expected study results

After successful completion of the course, students should be able to:

  • describe basic statistical methods used in technical engineering,
  • distinguish between different  statistical methods,
  • use statistical methods to make a statistical analysis,
  • use statistical programming tools for solving statistical problems,
  • critically analyse and statistically interpret technical problems that we encounter in practise,
  • critically evaluate the solution.

Basic sources and literature

  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.

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University of Ljubljana, Faculty of Electrical Engineering Tržaška cesta 25, 1000 Ljubljana

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